{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"3class_classification_bidirectional_lstm.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1kJcWO52hfUfFyIRg3KzY8Y1KTVFFxQJq","authorship_tag":"ABX9TyN5yaVCd8AHfzj6ziNJxI+0"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"1e9G6vCj3ed5","executionInfo":{"status":"ok","timestamp":1619876320083,"user_tz":-330,"elapsed":3424,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}}},"source":["\n","# Import libraries and modules\n","import numpy as np\n","np.random.seed(123)  # for reproducibility\n","from sklearn.model_selection import StratifiedKFold\n","import pandas as pd\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","%matplotlib notebook\n","import matplotlib.pyplot as plt\n","import keras\n","from keras.models import Sequential\n","from keras.layers import Dense\n","from keras.layers import LSTM, GRU, Bidirectional\n","from keras.layers import Dropout\n","from keras.layers import Flatten, TimeDistributed, GlobalMaxPooling1D, GlobalAveragePooling1D\n","from keras.preprocessing import sequence\n","from keras.utils import np_utils\n","from keras import metrics\n","from keras import backend\n","from numpy import argmax\n","from numpy import asarray\n","from pylab import rcParams\n","from sklearn.metrics import confusion_matrix\n","rcParams['figure.figsize'] = 15, 10\n","\n","def rmse(y_true, y_pred):\n","    return backend.sqrt(backend.mean(backend.square(y_pred - y_true), axis = -1))\n","\n","def sensitivity(y_true, y_pred):  \n","     y_pred_pos = backend.round(backend.clip(y_pred, 0, 1))\n","     y_pred_neg = 1 - y_pred_pos\n","     y_pos = backend.round(backend.clip(y_true, 0, 1))\n","     y_neg = 1 - y_pos\n","     tp = backend.sum(y_pos * y_pred_pos)\n","     tn = backend.sum(y_neg * y_pred_neg)\n","     fp = backend.sum(y_neg * y_pred_pos)\n","     fn = backend.sum(y_pos * y_pred_neg)\n","     sensitivity = tp / (tp + fn)\n","     return sensitivity\n","\n","# Recall is the same as the sensitivity\n","def recall(y_true, y_pred):  \n","     y_pred_pos = backend.round(backend.clip(y_pred, 0, 1))\n","     y_pred_neg = 1 - y_pred_pos\n","     y_pos = backend.round(backend.clip(y_true, 0, 1))\n","     y_neg = 1 - y_pos\n","     tp = backend.sum(y_pos * y_pred_pos)\n","     tn = backend.sum(y_neg * y_pred_neg)\n","     fp = backend.sum(y_neg * y_pred_pos)\n","     fn = backend.sum(y_pos * y_pred_neg)\n","     recall = tp / (tp + fn)\n","     return recall\n","\n","def specificity(y_true, y_pred):  \n","     y_pred_pos = backend.round(backend.clip(y_pred, 0, 1))\n","     y_pred_neg = 1 - y_pred_pos\n","     y_pos = backend.round(backend.clip(y_true, 0, 1))\n","     y_neg = 1 - y_pos\n","     tp = backend.sum(y_pos * y_pred_pos)\n","     tn = backend.sum(y_neg * y_pred_neg)\n","     fp = backend.sum(y_neg * y_pred_pos)\n","     fn = backend.sum(y_pos * y_pred_neg)\n","     specificity = tn / (tn + fp)\n","     return specificity\n","\n","\n","def precision(y_true, y_pred):  \n","     y_pred_pos = backend.round(backend.clip(y_pred, 0, 1))\n","     y_pred_neg = 1 - y_pred_pos\n","     y_pos = backend.round(backend.clip(y_true, 0, 1))\n","     y_neg = 1 - y_pos\n","     tp = backend.sum(y_pos * y_pred_pos)\n","     tn = backend.sum(y_neg * y_pred_neg)\n","     fp = backend.sum(y_neg * y_pred_pos)\n","     fn = backend.sum(y_pos * y_pred_neg)\n","     precision = tp / (tp + fp)\n","     return precision\n"],"execution_count":2,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"k3Rr0oDk3_2O"},"source":["#(A) 3 class classification [AB vs CD vs E]"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":422},"id":"HARp4V1C37NO","executionInfo":{"status":"ok","timestamp":1619876325990,"user_tz":-330,"elapsed":3252,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"14870b70-3904-4835-c4b3-6f58a5ae7a7d"},"source":["df=pd.read_csv(\"/content/drive/My Drive/mtech_finalyr_project/modified_Dataset/final_csv_with_shuffle_ABvsCDvsE.csv\")\n","df"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  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-109.0  ...  554.0  460.0  343.0  247.0    4\n","3    343.0  311.0  284.0  274.0  260.0  ...  515.0  527.0  480.0  397.0    4\n","4    -63.0 -107.0 -208.0 -310.0 -395.0  ...  -32.0   56.0   44.0  -37.0    4\n","..     ...    ...    ...    ...    ...  ...    ...    ...    ...    ...  ...\n","495  213.0  210.0  210.0  212.0  194.0  ...   -8.0   -9.0   -2.0   20.0    4\n","496   24.0   10.0   -9.0  -18.0   -9.0  ...  -29.0  -39.0  -33.0  -28.0    0\n","497  -20.0  -12.0   -6.0    6.0    8.0  ...   10.0   12.0    9.0    2.0    0\n","498 -104.0 -100.0  -94.0  -87.0  -87.0  ...  -30.0  -37.0  -45.0  -59.0    2\n","499  -43.0  -57.0  -86.0 -106.0 -106.0  ...   80.0   39.0   21.0   -8.0    2\n","\n","[500 rows x 4097 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":422},"id":"l2IQj4_o4rYQ","executionInfo":{"status":"ok","timestamp":1619876328129,"user_tz":-330,"elapsed":1749,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"0e6244c0-65a4-4c8d-8884-b11a03a1c532"},"source":["#converting tags into 0 nd 1\n","df.tag=df.tag.replace({2:1 , 4:2 })\n","df"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  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 <td>-29.0</td>\n","      <td>-28.0</td>\n","      <td>-23.0</td>\n","      <td>-15.0</td>\n","      <td>-13.0</td>\n","      <td>-14.0</td>\n","      <td>-9.0</td>\n","      <td>-10.0</td>\n","      <td>-5.0</td>\n","      <td>-2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>-2.0</td>\n","      <td>-8.0</td>\n","      <td>-9.0</td>\n","      <td>-2.0</td>\n","      <td>20.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>496</th>\n","      <td>24.0</td>\n","      <td>10.0</td>\n","      <td>-9.0</td>\n","      <td>-18.0</td>\n","      <td>-9.0</td>\n","      <td>-3.0</td>\n","      <td>-2.0</td>\n","      <td>-2.0</td>\n","      <td>-9.0</td>\n","      <td>-9.0</td>\n","      <td>-14.0</td>\n","      <td>-14.0</td>\n","      <td>-24.0</td>\n","      <td>-24.0</td>\n","      <td>-12.0</td>\n","      <td>-1.0</td>\n","      <td>13.0</td>\n","      <td>31.0</td>\n","      <td>60.0</td>\n","      <td>79.0</td>\n","      <td>98.0</td>\n","      <td>110.0</td>\n","      <td>104.0</td>\n","      <td>72.0</td>\n","      <td>40.0</td>\n","      <td>26.0</td>\n","      <td>24.0</td>\n","      <td>33.0</td>\n","      <td>40.0</td>\n","      <td>29.0</td>\n","      <td>13.0</td>\n","      <td>3.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>10.0</td>\n","      <td>32.0</td>\n","      <td>52.0</td>\n","      <td>61.0</td>\n","      <td>54.0</td>\n","      <td>19.0</td>\n","      <td>...</td>\n","      <td>73.0</td>\n","      <td>55.0</td>\n","      <td>33.0</td>\n","      <td>15.0</td>\n","      <td>-5.0</td>\n","      <td>-18.0</td>\n","      <td>-24.0</td>\n","      <td>-28.0</td>\n","      <td>-15.0</td>\n","      <td>12.0</td>\n","      <td>33.0</td>\n","      <td>48.0</td>\n","      <td>47.0</td>\n","      <td>36.0</td>\n","      <td>19.0</td>\n","      <td>16.0</td>\n","      <td>31.0</td>\n","      <td>56.0</td>\n","      <td>69.0</td>\n","      <td>75.0</td>\n","      <td>67.0</td>\n","      <td>66.0</td>\n","      <td>76.0</td>\n","      <td>93.0</td>\n","      <td>88.0</td>\n","      <td>65.0</td>\n","      <td>56.0</td>\n","      <td>58.0</td>\n","      <td>63.0</td>\n","      <td>48.0</td>\n","      <td>29.0</td>\n","      <td>3.0</td>\n","      <td>-17.0</td>\n","      <td>-14.0</td>\n","      <td>-21.0</td>\n","      <td>-29.0</td>\n","      <td>-39.0</td>\n","      <td>-33.0</td>\n","      <td>-28.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>497</th>\n","      <td>-20.0</td>\n","      <td>-12.0</td>\n","      <td>-6.0</td>\n","      <td>6.0</td>\n","      <td>8.0</td>\n","      <td>-2.0</td>\n","      <td>-22.0</td>\n","      <td>-39.0</td>\n","      <td>-39.0</td>\n","      <td>-49.0</td>\n","      <td>-46.0</td>\n","      <td>-43.0</td>\n","      <td>-43.0</td>\n","      <td>-46.0</td>\n","      <td>-46.0</td>\n","      <td>-35.0</td>\n","      <td>-23.0</td>\n","      <td>-13.0</td>\n","      <td>-14.0</td>\n","      <td>-17.0</td>\n","      <td>-23.0</td>\n","      <td>-25.0</td>\n","      <td>-18.0</td>\n","      <td>-18.0</td>\n","      <td>-14.0</td>\n","      <td>-16.0</td>\n","      <td>-20.0</td>\n","      <td>-36.0</td>\n","      <td>-46.0</td>\n","      <td>-54.0</td>\n","      <td>-64.0</td>\n","      <td>-64.0</td>\n","      <td>-63.0</td>\n","      <td>-55.0</td>\n","      <td>-53.0</td>\n","      <td>-44.0</td>\n","      <td>-43.0</td>\n","      <td>-47.0</td>\n","      <td>-44.0</td>\n","      <td>-29.0</td>\n","      <td>...</td>\n","      <td>31.0</td>\n","      <td>26.0</td>\n","      <td>20.0</td>\n","      <td>21.0</td>\n","      <td>27.0</td>\n","      <td>33.0</td>\n","      <td>34.0</td>\n","      <td>42.0</td>\n","      <td>49.0</td>\n","      <td>37.0</td>\n","      <td>18.0</td>\n","      <td>-1.0</td>\n","      <td>-18.0</td>\n","      <td>-25.0</td>\n","      <td>-16.0</td>\n","      <td>6.0</td>\n","      <td>23.0</td>\n","      <td>37.0</td>\n","      <td>44.0</td>\n","      <td>39.0</td>\n","      <td>26.0</td>\n","      <td>14.0</td>\n","      <td>13.0</td>\n","      <td>11.0</td>\n","      <td>25.0</td>\n","      <td>38.0</td>\n","      <td>42.0</td>\n","      <td>31.0</td>\n","      <td>16.0</td>\n","      <td>8.0</td>\n","      <td>1.0</td>\n","      <td>7.0</td>\n","      <td>20.0</td>\n","      <td>19.0</td>\n","      <td>11.0</td>\n","      <td>10.0</td>\n","      <td>12.0</td>\n","      <td>9.0</td>\n","      <td>2.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>498</th>\n","      <td>-104.0</td>\n","      <td>-100.0</td>\n","      <td>-94.0</td>\n","      <td>-87.0</td>\n","      <td>-87.0</td>\n","      <td>-87.0</td>\n","      <td>-88.0</td>\n","      <td>-90.0</td>\n","      <td>-83.0</td>\n","      <td>-75.0</td>\n","      <td>-69.0</td>\n","      <td>-59.0</td>\n","      <td>-50.0</td>\n","      <td>-38.0</td>\n","      <td>-42.0</td>\n","      <td>-50.0</td>\n","      <td>-67.0</td>\n","      <td>-84.0</td>\n","      <td>-92.0</td>\n","      <td>-96.0</td>\n","      <td>-97.0</td>\n","      <td>-99.0</td>\n","      <td>-100.0</td>\n","      <td>-96.0</td>\n","      <td>-93.0</td>\n","      <td>-93.0</td>\n","      <td>-93.0</td>\n","      <td>-107.0</td>\n","      <td>-126.0</td>\n","      <td>-131.0</td>\n","      <td>-136.0</td>\n","      <td>-128.0</td>\n","      <td>-121.0</td>\n","      <td>-107.0</td>\n","      <td>-95.0</td>\n","      <td>-87.0</td>\n","      <td>-82.0</td>\n","      <td>-82.0</td>\n","      <td>-86.0</td>\n","      <td>-90.0</td>\n","      <td>...</td>\n","      <td>-28.0</td>\n","      <td>-22.0</td>\n","      <td>-23.0</td>\n","      <td>-19.0</td>\n","      <td>-20.0</td>\n","      <td>-18.0</td>\n","      <td>-14.0</td>\n","      <td>-23.0</td>\n","      <td>-37.0</td>\n","      <td>-56.0</td>\n","      <td>-64.0</td>\n","      <td>-67.0</td>\n","      <td>-64.0</td>\n","      <td>-49.0</td>\n","      <td>-40.0</td>\n","      <td>-34.0</td>\n","      <td>-39.0</td>\n","      <td>-39.0</td>\n","      <td>-43.0</td>\n","      <td>-43.0</td>\n","      <td>-35.0</td>\n","      <td>-33.0</td>\n","      <td>-31.0</td>\n","      <td>-24.0</td>\n","      <td>-11.0</td>\n","      <td>1.0</td>\n","      <td>8.0</td>\n","      <td>6.0</td>\n","      <td>2.0</td>\n","      <td>-8.0</td>\n","      <td>-16.0</td>\n","      <td>-27.0</td>\n","      <td>-38.0</td>\n","      <td>-45.0</td>\n","      <td>-40.0</td>\n","      <td>-30.0</td>\n","      <td>-37.0</td>\n","      <td>-45.0</td>\n","      <td>-59.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>499</th>\n","      <td>-43.0</td>\n","      <td>-57.0</td>\n","      <td>-86.0</td>\n","      <td>-106.0</td>\n","      <td>-106.0</td>\n","      <td>-120.0</td>\n","      <td>-111.0</td>\n","      <td>-88.0</td>\n","      <td>-81.0</td>\n","      <td>-90.0</td>\n","      <td>-109.0</td>\n","      <td>-102.0</td>\n","      <td>-69.0</td>\n","      <td>-20.0</td>\n","      <td>1.0</td>\n","      <td>25.0</td>\n","      <td>42.0</td>\n","      <td>68.0</td>\n","      <td>61.0</td>\n","      <td>36.0</td>\n","      <td>4.0</td>\n","      <td>4.0</td>\n","      <td>9.0</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>-2.0</td>\n","      <td>-13.0</td>\n","      <td>-23.0</td>\n","      <td>-30.0</td>\n","      <td>-39.0</td>\n","      <td>-13.0</td>\n","      <td>29.0</td>\n","      <td>48.0</td>\n","      <td>63.0</td>\n","      <td>67.0</td>\n","      <td>90.0</td>\n","      <td>96.0</td>\n","      <td>84.0</td>\n","      <td>81.0</td>\n","      <td>76.0</td>\n","      <td>...</td>\n","      <td>-129.0</td>\n","      <td>-117.0</td>\n","      <td>-130.0</td>\n","      <td>-132.0</td>\n","      <td>-134.0</td>\n","      <td>-150.0</td>\n","      <td>-161.0</td>\n","      <td>-191.0</td>\n","      <td>-180.0</td>\n","      <td>-145.0</td>\n","      <td>-135.0</td>\n","      <td>-123.0</td>\n","      <td>-139.0</td>\n","      <td>-127.0</td>\n","      <td>-103.0</td>\n","      <td>-111.0</td>\n","      <td>-95.0</td>\n","      <td>-82.0</td>\n","      <td>-55.0</td>\n","      <td>-19.0</td>\n","      <td>-11.0</td>\n","      <td>4.0</td>\n","      <td>16.0</td>\n","      <td>-22.0</td>\n","      <td>-1.0</td>\n","      <td>-20.0</td>\n","      <td>-11.0</td>\n","      <td>6.0</td>\n","      <td>17.0</td>\n","      <td>22.0</td>\n","      <td>3.0</td>\n","      <td>-9.0</td>\n","      <td>6.0</td>\n","      <td>39.0</td>\n","      <td>59.0</td>\n","      <td>80.0</td>\n","      <td>39.0</td>\n","      <td>21.0</td>\n","      <td>-8.0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>500 rows × 4097 columns</p>\n","</div>"],"text/plain":["         0      1      2      3      4  ...   4092   4093   4094   4095  tag\n","0    -18.0  -55.0 -126.0 -202.0 -238.0  ... -222.0 -224.0 -200.0 -127.0    2\n","1    -26.0    1.0   29.0   41.0   33.0  ... -205.0 -209.0 -207.0 -210.0    2\n","2     68.0 -106.0 -149.0 -141.0 -109.0  ...  554.0  460.0  343.0  247.0    2\n","3    343.0  311.0  284.0  274.0  260.0  ...  515.0  527.0  480.0  397.0    2\n","4    -63.0 -107.0 -208.0 -310.0 -395.0  ...  -32.0   56.0   44.0  -37.0    2\n","..     ...    ...    ...    ...    ...  ...    ...    ...    ...    ...  ...\n","495  213.0  210.0  210.0  212.0  194.0  ...   -8.0   -9.0   -2.0   20.0    2\n","496   24.0   10.0   -9.0  -18.0   -9.0  ...  -29.0  -39.0  -33.0  -28.0    0\n","497  -20.0  -12.0   -6.0    6.0    8.0  ...   10.0   12.0    9.0    2.0    0\n","498 -104.0 -100.0  -94.0  -87.0  -87.0  ...  -30.0  -37.0  -45.0  -59.0    1\n","499  -43.0  -57.0  -86.0 -106.0 -106.0  ...   80.0   39.0   21.0   -8.0    1\n","\n","[500 rows x 4097 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yc7T0jL6-eEB","executionInfo":{"status":"ok","timestamp":1619876330904,"user_tz":-330,"elapsed":1435,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"5ef5e948-407a-41bf-f35e-efbbe35367b0"},"source":["df[\"tag\"].value_counts()"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1    200\n","0    200\n","2    100\n","Name: tag, dtype: int64"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sJCzEdId5D-I","executionInfo":{"status":"ok","timestamp":1619877470964,"user_tz":-330,"elapsed":1701,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"0bf877c1-1028-4b80-f1fc-eb921706ec9f"},"source":["# Time Steps of LSTM\n","data_length = 4096\n","timesteps = 2048\n","data_dim = data_length//timesteps\n","data_dim\n","\n","\n","#breaking dataset into X nd y\n","df1=df.values     #df1 is numpy.ndarray and df is pandas.dataframe\n","X, y = df1[:, :-1], df1[:, -1]\n","print(df.shape)\n","print(\"shape of X\",X.shape)\n","print(\"shape of y\",y.shape)\n","\n","\n","#breaking X nd y into train nd test\n","from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1, stratify=y)\n","\n","print(\"\\nshape of X_train\",X_train.shape)\n","print(\"shape of X_test\",X_test.shape)\n","print(\"shape of y_train\",y_train.shape)\n","print(\"shape of y_test\",y_test.shape)\n","\n","X_train=X_train.reshape([X_train.shape[0], timesteps, data_dim])\n","X_test = X_test.reshape([X_test.shape[0], timesteps, data_dim])\n","y_train=np_utils.to_categorical(y_train, num_classes=3)\n","y_test=np_utils.to_categorical(y_test, num_classes=3)\n","\n","\n","\n","print(\"\\nshape of X_train\",X_train.shape)\n","print(\"shape of X_test\",X_test.shape)\n","print(\"shape of y_train\",y_train.shape)\n","print(\"shape of y_test\",y_test.shape)"],"execution_count":19,"outputs":[{"output_type":"stream","text":["(500, 4097)\n","shape of X (500, 4096)\n","shape of y (500,)\n","\n","shape of X_train (350, 4096)\n","shape of X_test (150, 4096)\n","shape of y_train (350,)\n","shape of y_test (150,)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"1X6Ig4ew6KS0","executionInfo":{"status":"ok","timestamp":1619608370428,"user_tz":-330,"elapsed":150433,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"a6006dc7-46b9-494b-cba5-07c2cab0d7e5"},"source":["nb_epoch=100\n","\n","model = Sequential()\n","\n","model.add(Bidirectional(LSTM(80, input_shape= (timesteps, data_dim), return_sequences = True)))\n","model.add(Dropout(0.1))\n","\n","model.add(TimeDistributed(Dense(50)))\n","\n","model.add(GlobalAveragePooling1D())\n","\n","model.add(Dense(3, activation='softmax'))\n","model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[sensitivity, specificity, 'accuracy'])\n","#print(model.summary())\n","history = model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=64, epochs=nb_epoch)\n","\n","\n","plt.plot(history.history['specificity'], 'b--')\n","plt.plot(history.history['sensitivity'], 'g--')\n","plt.plot(history.history['accuracy'], 'r--')\n","plt.title('Model Different Metrics')\n","plt.ylabel('Metrics')\n","plt.xlabel('Epoch #')\n","plt.show()\n","\n","\n","\n","scores = model.evaluate(X_test, y_test, verbose=0)\n","print(\"Sensitivity = %.2f%%\" % (scores[1]*100))\n","print(\"Specificity = %.2f%%\" % (scores[2]*100))\n","print(\"Classification Accuracy = %.2f%%\" % (scores[3]*100))\n","\n","\n","%matplotlib inline\n","plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","plt.title('model accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.grid()\n","plt.show()\n","\n","plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.grid()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch 1/100\n","6/6 [==============================] - 37s 361ms/step - loss: 1.2460 - sensitivity: 0.1131 - specificity: 0.8831 - accuracy: 0.3202 - val_loss: 0.9419 - val_sensitivity: 0.4261 - val_specificity: 0.9510 - val_accuracy: 0.6200\n","Epoch 2/100\n","6/6 [==============================] - 1s 175ms/step - loss: 0.8850 - sensitivity: 0.4650 - specificity: 0.9265 - accuracy: 0.6425 - val_loss: 0.7693 - val_sensitivity: 0.5256 - val_specificity: 0.9666 - val_accuracy: 0.7267\n","Epoch 3/100\n","6/6 [==============================] - 1s 175ms/step - loss: 0.7427 - sensitivity: 0.5340 - specificity: 0.9576 - accuracy: 0.7362 - val_loss: 0.6640 - val_sensitivity: 0.6345 - val_specificity: 0.9666 - val_accuracy: 0.8600\n","Epoch 4/100\n","6/6 [==============================] - 1s 172ms/step - loss: 0.6381 - sensitivity: 0.6927 - specificity: 0.9541 - accuracy: 0.8188 - val_loss: 0.5511 - val_sensitivity: 0.7988 - val_specificity: 0.9562 - val_accuracy: 0.8533\n","Epoch 5/100\n","6/6 [==============================] - 1s 176ms/step - loss: 0.5641 - sensitivity: 0.7654 - specificity: 0.9300 - accuracy: 0.8169 - val_loss: 0.4739 - val_sensitivity: 0.8300 - val_specificity: 0.9408 - val_accuracy: 0.8600\n","Epoch 6/100\n","6/6 [==============================] - 1s 177ms/step - loss: 0.5109 - sensitivity: 0.7851 - specificity: 0.9230 - accuracy: 0.8134 - val_loss: 0.4057 - val_sensitivity: 0.8404 - val_specificity: 0.9562 - val_accuracy: 0.8667\n","Epoch 7/100\n","6/6 [==============================] - 1s 176ms/step - loss: 0.4404 - sensitivity: 0.8182 - specificity: 0.9368 - accuracy: 0.8350 - val_loss: 0.3556 - val_sensitivity: 0.8509 - val_specificity: 0.9486 - val_accuracy: 0.8867\n","Epoch 8/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.3579 - sensitivity: 0.8533 - specificity: 0.9465 - accuracy: 0.8764 - val_loss: 0.3125 - val_sensitivity: 0.8764 - val_specificity: 0.9588 - val_accuracy: 0.9000\n","Epoch 9/100\n","6/6 [==============================] - 1s 174ms/step - loss: 0.3549 - sensitivity: 0.8446 - specificity: 0.9418 - accuracy: 0.8526 - val_loss: 0.2708 - val_sensitivity: 0.8973 - val_specificity: 0.9590 - val_accuracy: 0.9200\n","Epoch 10/100\n","6/6 [==============================] - 1s 175ms/step - loss: 0.3020 - sensitivity: 0.8864 - specificity: 0.9609 - accuracy: 0.9035 - val_loss: 0.2354 - val_sensitivity: 0.8916 - val_specificity: 0.9640 - val_accuracy: 0.9133\n","Epoch 11/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.2803 - sensitivity: 0.9014 - specificity: 0.9611 - accuracy: 0.9111 - val_loss: 0.2260 - val_sensitivity: 0.8920 - val_specificity: 0.9590 - val_accuracy: 0.9133\n","Epoch 12/100\n","6/6 [==============================] - 1s 175ms/step - loss: 0.2431 - sensitivity: 0.8829 - specificity: 0.9613 - accuracy: 0.9028 - val_loss: 0.2009 - val_sensitivity: 0.9276 - val_specificity: 0.9742 - val_accuracy: 0.9333\n","Epoch 13/100\n","6/6 [==============================] - 1s 175ms/step - loss: 0.2674 - sensitivity: 0.8785 - specificity: 0.9437 - accuracy: 0.8844 - val_loss: 0.1875 - val_sensitivity: 0.9124 - val_specificity: 0.9666 - val_accuracy: 0.9267\n","Epoch 14/100\n","6/6 [==============================] - 1s 176ms/step - loss: 0.2213 - sensitivity: 0.9016 - specificity: 0.9625 - accuracy: 0.9133 - val_loss: 0.1456 - val_sensitivity: 0.9432 - val_specificity: 0.9768 - val_accuracy: 0.9400\n","Epoch 15/100\n","6/6 [==============================] - 1s 177ms/step - loss: 0.2142 - sensitivity: 0.8978 - specificity: 0.9560 - accuracy: 0.9055 - val_loss: 0.1345 - val_sensitivity: 0.9635 - val_specificity: 0.9896 - val_accuracy: 0.9600\n","Epoch 16/100\n","6/6 [==============================] - 1s 176ms/step - loss: 0.1910 - sensitivity: 0.9223 - specificity: 0.9666 - accuracy: 0.9272 - val_loss: 0.1518 - val_sensitivity: 0.9332 - val_specificity: 0.9770 - val_accuracy: 0.9533\n","Epoch 17/100\n","6/6 [==============================] - 1s 177ms/step - loss: 0.1912 - sensitivity: 0.9201 - specificity: 0.9655 - accuracy: 0.9259 - val_loss: 0.1301 - val_sensitivity: 0.9635 - val_specificity: 0.9818 - val_accuracy: 0.9533\n","Epoch 18/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.1575 - sensitivity: 0.9427 - specificity: 0.9761 - accuracy: 0.9425 - val_loss: 0.1250 - val_sensitivity: 0.9384 - val_specificity: 0.9846 - val_accuracy: 0.9533\n","Epoch 19/100\n","6/6 [==============================] - 1s 176ms/step - loss: 0.1620 - sensitivity: 0.9395 - specificity: 0.9728 - accuracy: 0.9412 - val_loss: 0.1079 - val_sensitivity: 0.9635 - val_specificity: 0.9870 - val_accuracy: 0.9600\n","Epoch 20/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.1277 - sensitivity: 0.9458 - specificity: 0.9747 - accuracy: 0.9464 - val_loss: 0.1072 - val_sensitivity: 0.9640 - val_specificity: 0.9872 - val_accuracy: 0.9733\n","Epoch 21/100\n","6/6 [==============================] - 1s 173ms/step - loss: 0.1365 - sensitivity: 0.9424 - specificity: 0.9768 - accuracy: 0.9542 - val_loss: 0.0966 - val_sensitivity: 0.9688 - val_specificity: 0.9896 - val_accuracy: 0.9600\n","Epoch 22/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.1297 - sensitivity: 0.9456 - specificity: 0.9802 - accuracy: 0.9590 - val_loss: 0.1031 - val_sensitivity: 0.9536 - val_specificity: 0.9820 - val_accuracy: 0.9667\n","Epoch 23/100\n","6/6 [==============================] - 1s 176ms/step - loss: 0.1251 - sensitivity: 0.9523 - specificity: 0.9786 - accuracy: 0.9555 - val_loss: 0.1021 - val_sensitivity: 0.9740 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 24/100\n","6/6 [==============================] - 1s 177ms/step - loss: 0.1063 - sensitivity: 0.9664 - specificity: 0.9874 - accuracy: 0.9731 - val_loss: 0.1067 - val_sensitivity: 0.9792 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 25/100\n","6/6 [==============================] - 1s 176ms/step - loss: 0.1226 - sensitivity: 0.9593 - specificity: 0.9815 - accuracy: 0.9591 - val_loss: 0.0722 - val_sensitivity: 0.9740 - val_specificity: 0.9922 - val_accuracy: 0.9667\n","Epoch 26/100\n","6/6 [==============================] - 1s 179ms/step - loss: 0.1070 - sensitivity: 0.9611 - specificity: 0.9814 - accuracy: 0.9608 - val_loss: 0.1101 - val_sensitivity: 0.9536 - val_specificity: 0.9794 - val_accuracy: 0.9600\n","Epoch 27/100\n","6/6 [==============================] - 1s 177ms/step - loss: 0.1200 - sensitivity: 0.9654 - specificity: 0.9827 - accuracy: 0.9660 - val_loss: 0.0703 - val_sensitivity: 0.9740 - val_specificity: 0.9922 - val_accuracy: 0.9667\n","Epoch 28/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.1144 - sensitivity: 0.9471 - specificity: 0.9762 - accuracy: 0.9544 - val_loss: 0.0718 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 29/100\n","6/6 [==============================] - 1s 181ms/step - loss: 0.1312 - sensitivity: 0.9573 - specificity: 0.9799 - accuracy: 0.9587 - val_loss: 0.1234 - val_sensitivity: 0.9588 - val_specificity: 0.9820 - val_accuracy: 0.9667\n","Epoch 30/100\n","6/6 [==============================] - 1s 179ms/step - loss: 0.1848 - sensitivity: 0.9421 - specificity: 0.9725 - accuracy: 0.9452 - val_loss: 0.0827 - val_sensitivity: 0.9792 - val_specificity: 0.9922 - val_accuracy: 0.9733\n","Epoch 31/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.1482 - sensitivity: 0.9567 - specificity: 0.9808 - accuracy: 0.9574 - val_loss: 0.0745 - val_sensitivity: 0.9896 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 32/100\n","6/6 [==============================] - 1s 177ms/step - loss: 0.1095 - sensitivity: 0.9626 - specificity: 0.9822 - accuracy: 0.9641 - val_loss: 0.0712 - val_sensitivity: 0.9844 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 33/100\n","6/6 [==============================] - 1s 180ms/step - loss: 0.1192 - sensitivity: 0.9658 - specificity: 0.9829 - accuracy: 0.9652 - val_loss: 0.0721 - val_sensitivity: 0.9896 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 34/100\n","6/6 [==============================] - 1s 177ms/step - loss: 0.0872 - sensitivity: 0.9785 - specificity: 0.9892 - accuracy: 0.9778 - val_loss: 0.0660 - val_sensitivity: 0.9792 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 35/100\n","6/6 [==============================] - 1s 182ms/step - loss: 0.0992 - sensitivity: 0.9661 - specificity: 0.9860 - accuracy: 0.9713 - val_loss: 0.0654 - val_sensitivity: 0.9896 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 36/100\n","6/6 [==============================] - 1s 179ms/step - loss: 0.0847 - sensitivity: 0.9834 - specificity: 0.9917 - accuracy: 0.9836 - val_loss: 0.0611 - val_sensitivity: 0.9792 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 37/100\n","6/6 [==============================] - 1s 180ms/step - loss: 0.0811 - sensitivity: 0.9794 - specificity: 0.9903 - accuracy: 0.9817 - val_loss: 0.0613 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 38/100\n","6/6 [==============================] - 1s 176ms/step - loss: 0.0934 - sensitivity: 0.9765 - specificity: 0.9883 - accuracy: 0.9768 - val_loss: 0.0622 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 39/100\n","6/6 [==============================] - 1s 179ms/step - loss: 0.0942 - sensitivity: 0.9687 - specificity: 0.9873 - accuracy: 0.9739 - val_loss: 0.0607 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 40/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.0821 - sensitivity: 0.9650 - specificity: 0.9825 - accuracy: 0.9650 - val_loss: 0.0538 - val_sensitivity: 0.9896 - val_specificity: 0.9974 - val_accuracy: 0.9867\n","Epoch 41/100\n","6/6 [==============================] - 1s 173ms/step - loss: 0.0801 - sensitivity: 0.9798 - specificity: 0.9899 - accuracy: 0.9791 - val_loss: 0.0551 - val_sensitivity: 0.9844 - val_specificity: 0.9948 - val_accuracy: 0.9800\n","Epoch 42/100\n","6/6 [==============================] - 1s 177ms/step - loss: 0.0635 - sensitivity: 0.9804 - specificity: 0.9902 - accuracy: 0.9799 - val_loss: 0.0504 - val_sensitivity: 0.9896 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 43/100\n","6/6 [==============================] - 1s 174ms/step - loss: 0.0696 - sensitivity: 0.9783 - specificity: 0.9891 - accuracy: 0.9785 - val_loss: 0.0515 - val_sensitivity: 0.9948 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 44/100\n","6/6 [==============================] - 1s 176ms/step - loss: 0.0687 - sensitivity: 0.9812 - specificity: 0.9906 - accuracy: 0.9813 - val_loss: 0.0759 - val_sensitivity: 0.9640 - val_specificity: 0.9872 - val_accuracy: 0.9733\n","Epoch 45/100\n","6/6 [==============================] - 1s 174ms/step - loss: 0.1525 - sensitivity: 0.9548 - specificity: 0.9774 - accuracy: 0.9544 - val_loss: 0.1198 - val_sensitivity: 0.9337 - val_specificity: 0.9695 - val_accuracy: 0.9600\n","Epoch 46/100\n","6/6 [==============================] - 1s 174ms/step - loss: 0.1595 - sensitivity: 0.9478 - specificity: 0.9739 - accuracy: 0.9475 - val_loss: 0.0803 - val_sensitivity: 0.9792 - val_specificity: 0.9922 - val_accuracy: 0.9733\n","Epoch 47/100\n","6/6 [==============================] - 1s 176ms/step - loss: 0.1174 - sensitivity: 0.9517 - specificity: 0.9759 - accuracy: 0.9507 - val_loss: 0.0905 - val_sensitivity: 0.9640 - val_specificity: 0.9820 - val_accuracy: 0.9667\n","Epoch 48/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.1103 - sensitivity: 0.9637 - specificity: 0.9819 - accuracy: 0.9630 - val_loss: 0.0647 - val_sensitivity: 0.9844 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 49/100\n","6/6 [==============================] - 1s 174ms/step - loss: 0.0920 - sensitivity: 0.9687 - specificity: 0.9852 - accuracy: 0.9687 - val_loss: 0.0551 - val_sensitivity: 0.9896 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 50/100\n","6/6 [==============================] - 1s 179ms/step - loss: 0.0858 - sensitivity: 0.9760 - specificity: 0.9880 - accuracy: 0.9761 - val_loss: 0.0505 - val_sensitivity: 0.9844 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 51/100\n","6/6 [==============================] - 1s 174ms/step - loss: 0.0701 - sensitivity: 0.9805 - specificity: 0.9902 - accuracy: 0.9808 - val_loss: 0.0486 - val_sensitivity: 0.9896 - val_specificity: 0.9974 - val_accuracy: 0.9867\n","Epoch 52/100\n","6/6 [==============================] - 1s 177ms/step - loss: 0.0475 - sensitivity: 0.9879 - specificity: 0.9939 - accuracy: 0.9899 - val_loss: 0.0480 - val_sensitivity: 0.9896 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 53/100\n","6/6 [==============================] - 1s 180ms/step - loss: 0.0698 - sensitivity: 0.9728 - specificity: 0.9864 - accuracy: 0.9721 - val_loss: 0.0668 - val_sensitivity: 0.9792 - val_specificity: 0.9896 - val_accuracy: 0.9733\n","Epoch 54/100\n","6/6 [==============================] - 1s 180ms/step - loss: 0.0837 - sensitivity: 0.9657 - specificity: 0.9828 - accuracy: 0.9657 - val_loss: 0.0458 - val_sensitivity: 0.9948 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 55/100\n","6/6 [==============================] - 1s 175ms/step - loss: 0.0599 - sensitivity: 0.9831 - specificity: 0.9916 - accuracy: 0.9844 - val_loss: 0.0466 - val_sensitivity: 0.9896 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 56/100\n","6/6 [==============================] - 1s 177ms/step - loss: 0.0643 - sensitivity: 0.9803 - specificity: 0.9901 - accuracy: 0.9806 - val_loss: 0.0536 - val_sensitivity: 0.9792 - val_specificity: 0.9896 - val_accuracy: 0.9733\n","Epoch 57/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.0621 - sensitivity: 0.9780 - specificity: 0.9890 - accuracy: 0.9783 - val_loss: 0.0443 - val_sensitivity: 0.9896 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 58/100\n","6/6 [==============================] - 1s 177ms/step - loss: 0.0461 - sensitivity: 0.9896 - specificity: 0.9948 - accuracy: 0.9909 - val_loss: 0.0460 - val_sensitivity: 0.9896 - val_specificity: 0.9974 - val_accuracy: 0.9867\n","Epoch 59/100\n","6/6 [==============================] - 1s 176ms/step - loss: 0.0813 - sensitivity: 0.9721 - specificity: 0.9879 - accuracy: 0.9749 - val_loss: 0.0693 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 60/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.0882 - sensitivity: 0.9633 - specificity: 0.9816 - accuracy: 0.9640 - val_loss: 0.0460 - val_sensitivity: 0.9948 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 61/100\n","6/6 [==============================] - 1s 175ms/step - loss: 0.1096 - sensitivity: 0.9403 - specificity: 0.9701 - accuracy: 0.9397 - val_loss: 0.0926 - val_sensitivity: 0.9692 - val_specificity: 0.9872 - val_accuracy: 0.9733\n","Epoch 62/100\n","6/6 [==============================] - 1s 177ms/step - loss: 0.1691 - sensitivity: 0.9605 - specificity: 0.9803 - accuracy: 0.9603 - val_loss: 0.0565 - val_sensitivity: 0.9744 - val_specificity: 0.9872 - val_accuracy: 0.9800\n","Epoch 63/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.1221 - sensitivity: 0.9518 - specificity: 0.9759 - accuracy: 0.9523 - val_loss: 0.0458 - val_sensitivity: 0.9896 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 64/100\n","6/6 [==============================] - 1s 180ms/step - loss: 0.0722 - sensitivity: 0.9762 - specificity: 0.9881 - accuracy: 0.9755 - val_loss: 0.0505 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 65/100\n","6/6 [==============================] - 1s 180ms/step - loss: 0.0667 - sensitivity: 0.9761 - specificity: 0.9881 - accuracy: 0.9757 - val_loss: 0.0450 - val_sensitivity: 0.9948 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 66/100\n","6/6 [==============================] - 1s 181ms/step - loss: 0.0589 - sensitivity: 0.9811 - specificity: 0.9906 - accuracy: 0.9815 - val_loss: 0.0483 - val_sensitivity: 0.9896 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 67/100\n","6/6 [==============================] - 1s 176ms/step - loss: 0.0494 - sensitivity: 0.9817 - specificity: 0.9921 - accuracy: 0.9812 - val_loss: 0.0472 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 68/100\n","6/6 [==============================] - 1s 181ms/step - loss: 0.0450 - sensitivity: 0.9845 - specificity: 0.9922 - accuracy: 0.9841 - val_loss: 0.0442 - val_sensitivity: 0.9896 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 69/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.0458 - sensitivity: 0.9859 - specificity: 0.9929 - accuracy: 0.9855 - val_loss: 0.0420 - val_sensitivity: 0.9948 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 70/100\n","6/6 [==============================] - 1s 177ms/step - loss: 0.0464 - sensitivity: 0.9815 - specificity: 0.9908 - accuracy: 0.9811 - val_loss: 0.0428 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 71/100\n","6/6 [==============================] - 1s 179ms/step - loss: 0.0523 - sensitivity: 0.9770 - specificity: 0.9914 - accuracy: 0.9766 - val_loss: 0.0409 - val_sensitivity: 0.9896 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 72/100\n","6/6 [==============================] - 1s 179ms/step - loss: 0.0438 - sensitivity: 0.9844 - specificity: 0.9922 - accuracy: 0.9840 - val_loss: 0.0394 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 73/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.0440 - sensitivity: 0.9887 - specificity: 0.9944 - accuracy: 0.9884 - val_loss: 0.0438 - val_sensitivity: 0.9896 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 74/100\n","6/6 [==============================] - 1s 173ms/step - loss: 0.0305 - sensitivity: 0.9908 - specificity: 0.9954 - accuracy: 0.9912 - val_loss: 0.0420 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 75/100\n","6/6 [==============================] - 1s 175ms/step - loss: 0.0428 - sensitivity: 0.9811 - specificity: 0.9906 - accuracy: 0.9807 - val_loss: 0.0481 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 76/100\n","6/6 [==============================] - 1s 173ms/step - loss: 0.0446 - sensitivity: 0.9883 - specificity: 0.9942 - accuracy: 0.9889 - val_loss: 0.0486 - val_sensitivity: 0.9896 - val_specificity: 0.9974 - val_accuracy: 0.9867\n","Epoch 77/100\n","6/6 [==============================] - 1s 176ms/step - loss: 0.0377 - sensitivity: 0.9870 - specificity: 0.9935 - accuracy: 0.9866 - val_loss: 0.0438 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 78/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.0331 - sensitivity: 0.9894 - specificity: 0.9947 - accuracy: 0.9899 - val_loss: 0.0439 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 79/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.0307 - sensitivity: 0.9923 - specificity: 0.9974 - accuracy: 0.9920 - val_loss: 0.0427 - val_sensitivity: 0.9844 - val_specificity: 0.9948 - val_accuracy: 0.9800\n","Epoch 80/100\n","6/6 [==============================] - 1s 182ms/step - loss: 0.0271 - sensitivity: 0.9901 - specificity: 0.9951 - accuracy: 0.9898 - val_loss: 0.0521 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 81/100\n","6/6 [==============================] - 1s 179ms/step - loss: 0.0326 - sensitivity: 0.9877 - specificity: 0.9938 - accuracy: 0.9882 - val_loss: 0.0480 - val_sensitivity: 0.9844 - val_specificity: 0.9948 - val_accuracy: 0.9800\n","Epoch 82/100\n","6/6 [==============================] - 1s 181ms/step - loss: 0.0358 - sensitivity: 0.9887 - specificity: 0.9944 - accuracy: 0.9884 - val_loss: 0.0457 - val_sensitivity: 0.9948 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 83/100\n","6/6 [==============================] - 1s 181ms/step - loss: 0.0348 - sensitivity: 0.9875 - specificity: 0.9937 - accuracy: 0.9877 - val_loss: 0.0378 - val_sensitivity: 0.9896 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 84/100\n","6/6 [==============================] - 1s 180ms/step - loss: 0.0579 - sensitivity: 0.9854 - specificity: 0.9927 - accuracy: 0.9850 - val_loss: 0.0407 - val_sensitivity: 0.9896 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 85/100\n","6/6 [==============================] - 1s 182ms/step - loss: 0.0407 - sensitivity: 0.9778 - specificity: 0.9915 - accuracy: 0.9834 - val_loss: 0.0517 - val_sensitivity: 0.9744 - val_specificity: 0.9872 - val_accuracy: 0.9800\n","Epoch 86/100\n","6/6 [==============================] - 1s 179ms/step - loss: 0.0689 - sensitivity: 0.9745 - specificity: 0.9873 - accuracy: 0.9756 - val_loss: 0.0461 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 87/100\n","6/6 [==============================] - 1s 180ms/step - loss: 0.0629 - sensitivity: 0.9696 - specificity: 0.9848 - accuracy: 0.9691 - val_loss: 0.0613 - val_sensitivity: 0.9692 - val_specificity: 0.9846 - val_accuracy: 0.9733\n","Epoch 88/100\n","6/6 [==============================] - 1s 179ms/step - loss: 0.0481 - sensitivity: 0.9868 - specificity: 0.9934 - accuracy: 0.9864 - val_loss: 0.0420 - val_sensitivity: 0.9948 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 89/100\n","6/6 [==============================] - 1s 179ms/step - loss: 0.0481 - sensitivity: 0.9835 - specificity: 0.9918 - accuracy: 0.9839 - val_loss: 0.0463 - val_sensitivity: 0.9844 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 90/100\n","6/6 [==============================] - 1s 179ms/step - loss: 0.0749 - sensitivity: 0.9675 - specificity: 0.9837 - accuracy: 0.9666 - val_loss: 0.0669 - val_sensitivity: 0.9588 - val_specificity: 0.9820 - val_accuracy: 0.9667\n","Epoch 91/100\n","6/6 [==============================] - 1s 184ms/step - loss: 0.0925 - sensitivity: 0.9653 - specificity: 0.9826 - accuracy: 0.9646 - val_loss: 0.0496 - val_sensitivity: 0.9844 - val_specificity: 0.9948 - val_accuracy: 0.9800\n","Epoch 92/100\n","6/6 [==============================] - 1s 183ms/step - loss: 0.0592 - sensitivity: 0.9794 - specificity: 0.9897 - accuracy: 0.9789 - val_loss: 0.0581 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 93/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.0472 - sensitivity: 0.9881 - specificity: 0.9940 - accuracy: 0.9878 - val_loss: 0.0393 - val_sensitivity: 0.9844 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 94/100\n","6/6 [==============================] - 1s 181ms/step - loss: 0.0421 - sensitivity: 0.9766 - specificity: 0.9883 - accuracy: 0.9761 - val_loss: 0.0374 - val_sensitivity: 0.9948 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 95/100\n","6/6 [==============================] - 1s 182ms/step - loss: 0.0373 - sensitivity: 0.9946 - specificity: 0.9973 - accuracy: 0.9944 - val_loss: 0.0356 - val_sensitivity: 0.9948 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 96/100\n","6/6 [==============================] - 1s 180ms/step - loss: 0.0282 - sensitivity: 0.9876 - specificity: 0.9938 - accuracy: 0.9872 - val_loss: 0.0412 - val_sensitivity: 0.9844 - val_specificity: 0.9922 - val_accuracy: 0.9800\n","Epoch 97/100\n","6/6 [==============================] - 1s 180ms/step - loss: 0.0270 - sensitivity: 0.9930 - specificity: 0.9965 - accuracy: 0.9928 - val_loss: 0.0331 - val_sensitivity: 0.9948 - val_specificity: 0.9974 - val_accuracy: 0.9933\n","Epoch 98/100\n","6/6 [==============================] - 1s 178ms/step - loss: 0.0369 - sensitivity: 0.9830 - specificity: 0.9915 - accuracy: 0.9827 - val_loss: 0.0355 - val_sensitivity: 0.9896 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 99/100\n","6/6 [==============================] - 1s 180ms/step - loss: 0.0307 - sensitivity: 0.9888 - specificity: 0.9944 - accuracy: 0.9886 - val_loss: 0.0383 - val_sensitivity: 0.9896 - val_specificity: 0.9948 - val_accuracy: 0.9867\n","Epoch 100/100\n","6/6 [==============================] - 1s 182ms/step - loss: 0.0161 - sensitivity: 0.9899 - specificity: 0.9950 - accuracy: 0.9904 - val_loss: 0.0370 - val_sensitivity: 0.9896 - val_specificity: 0.9974 - val_accuracy: 0.9867\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/javascript":["/* Put everything inside the global mpl namespace */\n","window.mpl = {};\n","\n","\n","mpl.get_websocket_type = function() {\n","    if (typeof(WebSocket) !== 'undefined') {\n","        return WebSocket;\n","    } else if (typeof(MozWebSocket) !== 'undefined') {\n","        return MozWebSocket;\n","    } else {\n","        alert('Your browser does not have WebSocket support. ' +\n","              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n","              'Firefox 4 and 5 are also supported but you ' +\n","              'have to enable WebSockets in about:config.');\n","    };\n","}\n","\n","mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n","    this.id = figure_id;\n","\n","    this.ws = websocket;\n","\n","    this.supports_binary = (this.ws.binaryType != undefined);\n","\n","    if (!this.supports_binary) {\n","        var warnings = document.getElementById(\"mpl-warnings\");\n","        if (warnings) {\n","            warnings.style.display = 'block';\n","            warnings.textContent = (\n","                \"This browser does not support binary websocket messages. \" +\n","                    \"Performance may be slow.\");\n","        }\n","    }\n","\n","    this.imageObj = new Image();\n","\n","    this.context = undefined;\n","    this.message = undefined;\n","    this.canvas = undefined;\n","    this.rubberband_canvas = undefined;\n","    this.rubberband_context = undefined;\n","    this.format_dropdown = undefined;\n","\n","    this.image_mode = 'full';\n","\n","    this.root = $('<div/>');\n","    this._root_extra_style(this.root)\n","    this.root.attr('style', 'display: inline-block');\n","\n","    $(parent_element).append(this.root);\n","\n","    this._init_header(this);\n","    this._init_canvas(this);\n","    this._init_toolbar(this);\n","\n","    var fig = this;\n","\n","    this.waiting = false;\n","\n","    this.ws.onopen =  function () {\n","            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n","            fig.send_message(\"send_image_mode\", {});\n","            if (mpl.ratio != 1) {\n","                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n","            }\n","            fig.send_message(\"refresh\", {});\n","        }\n","\n","    this.imageObj.onload = function() {\n","            if (fig.image_mode == 'full') {\n","                // Full images could contain transparency (where diff images\n","                // almost always do), so we need to clear the canvas so that\n","                // there is no ghosting.\n","                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n","            }\n","            fig.context.drawImage(fig.imageObj, 0, 0);\n","        };\n","\n","    this.imageObj.onunload = function() {\n","        fig.ws.close();\n","    }\n","\n","    this.ws.onmessage = this._make_on_message_function(this);\n","\n","    this.ondownload = ondownload;\n","}\n","\n","mpl.figure.prototype._init_header = function() {\n","    var titlebar = $(\n","        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n","        'ui-helper-clearfix\"/>');\n","    var titletext = $(\n","        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n","        'text-align: center; padding: 3px;\"/>');\n","    titlebar.append(titletext)\n","    this.root.append(titlebar);\n","    this.header = titletext[0];\n","}\n","\n","\n","\n","mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n","\n","}\n","\n","\n","mpl.figure.prototype._root_extra_style = function(canvas_div) {\n","\n","}\n","\n","mpl.figure.prototype._init_canvas = function() {\n","    var fig = this;\n","\n","    var canvas_div = $('<div/>');\n","\n","    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n","\n","    function canvas_keyboard_event(event) {\n","        return fig.key_event(event, event['data']);\n","    }\n","\n","    canvas_div.keydown('key_press', canvas_keyboard_event);\n","    canvas_div.keyup('key_release', canvas_keyboard_event);\n","    this.canvas_div = canvas_div\n","    this._canvas_extra_style(canvas_div)\n","    this.root.append(canvas_div);\n","\n","    var canvas = $('<canvas/>');\n","    canvas.addClass('mpl-canvas');\n","    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n","\n","    this.canvas = canvas[0];\n","    this.context = canvas[0].getContext(\"2d\");\n","\n","    var backingStore = this.context.backingStorePixelRatio ||\n","\tthis.context.webkitBackingStorePixelRatio ||\n","\tthis.context.mozBackingStorePixelRatio ||\n","\tthis.context.msBackingStorePixelRatio ||\n","\tthis.context.oBackingStorePixelRatio ||\n","\tthis.context.backingStorePixelRatio || 1;\n","\n","    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n","\n","    var rubberband = $('<canvas/>');\n","    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n","\n","    var pass_mouse_events = true;\n","\n","    canvas_div.resizable({\n","        start: function(event, ui) {\n","            pass_mouse_events = false;\n","        },\n","        resize: function(event, ui) {\n","            fig.request_resize(ui.size.width, ui.size.height);\n","        },\n","        stop: function(event, ui) {\n","            pass_mouse_events = true;\n","            fig.request_resize(ui.size.width, ui.size.height);\n","        },\n","    });\n","\n","    function mouse_event_fn(event) {\n","        if (pass_mouse_events)\n","            return fig.mouse_event(event, event['data']);\n","    }\n","\n","    rubberband.mousedown('button_press', mouse_event_fn);\n","    rubberband.mouseup('button_release', mouse_event_fn);\n","    // Throttle sequential mouse events to 1 every 20ms.\n","    rubberband.mousemove('motion_notify', mouse_event_fn);\n","\n","    rubberband.mouseenter('figure_enter', mouse_event_fn);\n","    rubberband.mouseleave('figure_leave', mouse_event_fn);\n","\n","    canvas_div.on(\"wheel\", function (event) {\n","        event = event.originalEvent;\n","        event['data'] = 'scroll'\n","        if (event.deltaY < 0) {\n","            event.step = 1;\n","        } else {\n","            event.step = -1;\n","        }\n","        mouse_event_fn(event);\n","    });\n","\n","    canvas_div.append(canvas);\n","    canvas_div.append(rubberband);\n","\n","    this.rubberband = rubberband;\n","    this.rubberband_canvas = rubberband[0];\n","    this.rubberband_context = rubberband[0].getContext(\"2d\");\n","    this.rubberband_context.strokeStyle = \"#000000\";\n","\n","    this._resize_canvas = function(width, height) {\n","        // Keep the size of the canvas, canvas container, and rubber band\n","        // canvas in synch.\n","        canvas_div.css('width', width)\n","        canvas_div.css('height', height)\n","\n","        canvas.attr('width', width * mpl.ratio);\n","        canvas.attr('height', height * mpl.ratio);\n","        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n","\n","        rubberband.attr('width', width);\n","        rubberband.attr('height', height);\n","    }\n","\n","    // Set the figure to an initial 600x600px, this will subsequently be updated\n","    // upon first draw.\n","    this._resize_canvas(600, 600);\n","\n","    // Disable right mouse context menu.\n","    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n","        return false;\n","    });\n","\n","    function set_focus () {\n","        canvas.focus();\n","        canvas_div.focus();\n","    }\n","\n","    window.setTimeout(set_focus, 100);\n","}\n","\n","mpl.figure.prototype._init_toolbar = function() {\n","    var fig = this;\n","\n","    var nav_element = $('<div/>');\n","    nav_element.attr('style', 'width: 100%');\n","    this.root.append(nav_element);\n","\n","    // Define a callback function for later on.\n","    function toolbar_event(event) {\n","        return fig.toolbar_button_onclick(event['data']);\n","    }\n","    function toolbar_mouse_event(event) {\n","        return fig.toolbar_button_onmouseover(event['data']);\n","    }\n","\n","    for(var toolbar_ind in mpl.toolbar_items) {\n","        var name = mpl.toolbar_items[toolbar_ind][0];\n","        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n","        var image = mpl.toolbar_items[toolbar_ind][2];\n","        var method_name = mpl.toolbar_items[toolbar_ind][3];\n","\n","        if (!name) {\n","            // put a spacer in here.\n","            continue;\n","        }\n","        var button = $('<button/>');\n","        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n","                        'ui-button-icon-only');\n","        button.attr('role', 'button');\n","        button.attr('aria-disabled', 'false');\n","        button.click(method_name, toolbar_event);\n","        button.mouseover(tooltip, toolbar_mouse_event);\n","\n","        var icon_img = $('<span/>');\n","        icon_img.addClass('ui-button-icon-primary ui-icon');\n","        icon_img.addClass(image);\n","        icon_img.addClass('ui-corner-all');\n","\n","        var tooltip_span = $('<span/>');\n","        tooltip_span.addClass('ui-button-text');\n","        tooltip_span.html(tooltip);\n","\n","        button.append(icon_img);\n","        button.append(tooltip_span);\n","\n","        nav_element.append(button);\n","    }\n","\n","    var fmt_picker_span = $('<span/>');\n","\n","    var fmt_picker = $('<select/>');\n","    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n","    fmt_picker_span.append(fmt_picker);\n","    nav_element.append(fmt_picker_span);\n","    this.format_dropdown = fmt_picker[0];\n","\n","    for (var ind in mpl.extensions) {\n","        var fmt = mpl.extensions[ind];\n","        var option = $(\n","            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n","        fmt_picker.append(option);\n","    }\n","\n","    // Add hover states to the ui-buttons\n","    $( \".ui-button\" ).hover(\n","        function() { $(this).addClass(\"ui-state-hover\");},\n","        function() { $(this).removeClass(\"ui-state-hover\");}\n","    );\n","\n","    var status_bar = $('<span class=\"mpl-message\"/>');\n","    nav_element.append(status_bar);\n","    this.message = status_bar[0];\n","}\n","\n","mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n","    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n","    // which will in turn request a refresh of the image.\n","    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n","}\n","\n","mpl.figure.prototype.send_message = function(type, properties) {\n","    properties['type'] = type;\n","    properties['figure_id'] = this.id;\n","    this.ws.send(JSON.stringify(properties));\n","}\n","\n","mpl.figure.prototype.send_draw_message = function() {\n","    if (!this.waiting) {\n","        this.waiting = true;\n","        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n","    }\n","}\n","\n","\n","mpl.figure.prototype.handle_save = function(fig, msg) {\n","    var format_dropdown = fig.format_dropdown;\n","    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n","    fig.ondownload(fig, format);\n","}\n","\n","\n","mpl.figure.prototype.handle_resize = function(fig, msg) {\n","    var size = msg['size'];\n","    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n","        fig._resize_canvas(size[0], size[1]);\n","        fig.send_message(\"refresh\", {});\n","    };\n","}\n","\n","mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n","    var x0 = msg['x0'] / mpl.ratio;\n","    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n","    var x1 = msg['x1'] / mpl.ratio;\n","    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n","    x0 = Math.floor(x0) + 0.5;\n","    y0 = Math.floor(y0) + 0.5;\n","    x1 = Math.floor(x1) + 0.5;\n","    y1 = Math.floor(y1) + 0.5;\n","    var min_x = Math.min(x0, x1);\n","    var min_y = Math.min(y0, y1);\n","    var width = Math.abs(x1 - x0);\n","    var height = Math.abs(y1 - y0);\n","\n","    fig.rubberband_context.clearRect(\n","        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n","\n","    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n","}\n","\n","mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n","    // Updates the figure title.\n","    fig.header.textContent = msg['label'];\n","}\n","\n","mpl.figure.prototype.handle_cursor = function(fig, msg) {\n","    var cursor = msg['cursor'];\n","    switch(cursor)\n","    {\n","    case 0:\n","        cursor = 'pointer';\n","        break;\n","    case 1:\n","        cursor = 'default';\n","        break;\n","    case 2:\n","        cursor = 'crosshair';\n","        break;\n","    case 3:\n","        cursor = 'move';\n","        break;\n","    }\n","    fig.rubberband_canvas.style.cursor = cursor;\n","}\n","\n","mpl.figure.prototype.handle_message = function(fig, msg) {\n","    fig.message.textContent = msg['message'];\n","}\n","\n","mpl.figure.prototype.handle_draw = function(fig, msg) {\n","    // Request the server to send over a new figure.\n","    fig.send_draw_message();\n","}\n","\n","mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n","    fig.image_mode = msg['mode'];\n","}\n","\n","mpl.figure.prototype.updated_canvas_event = function() {\n","    // Called whenever the canvas gets updated.\n","    this.send_message(\"ack\", {});\n","}\n","\n","// A function to construct a web socket function for onmessage handling.\n","// Called in the figure constructor.\n","mpl.figure.prototype._make_on_message_function = function(fig) {\n","    return function socket_on_message(evt) {\n","        if (evt.data instanceof Blob) {\n","            /* FIXME: We get \"Resource interpreted as Image but\n","             * transferred with MIME type text/plain:\" errors on\n","             * Chrome.  But how to set the MIME type?  It doesn't seem\n","             * to be part of the websocket stream */\n","            evt.data.type = \"image/png\";\n","\n","            /* Free the memory for the previous frames */\n","            if (fig.imageObj.src) {\n","                (window.URL || window.webkitURL).revokeObjectURL(\n","                    fig.imageObj.src);\n","            }\n","\n","            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n","                evt.data);\n","            fig.updated_canvas_event();\n","            fig.waiting = false;\n","            return;\n","        }\n","        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n","            fig.imageObj.src = evt.data;\n","            fig.updated_canvas_event();\n","            fig.waiting = false;\n","            return;\n","        }\n","\n","        var msg = JSON.parse(evt.data);\n","        var msg_type = msg['type'];\n","\n","        // Call the  \"handle_{type}\" callback, which takes\n","        // the figure and JSON message as its only arguments.\n","        try {\n","            var callback = fig[\"handle_\" + msg_type];\n","        } catch (e) {\n","            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n","            return;\n","        }\n","\n","        if (callback) {\n","            try {\n","                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n","                callback(fig, msg);\n","            } catch (e) {\n","                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n","            }\n","        }\n","    };\n","}\n","\n","// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n","mpl.findpos = function(e) {\n","    //this section is from http://www.quirksmode.org/js/events_properties.html\n","    var targ;\n","    if (!e)\n","        e = window.event;\n","    if (e.target)\n","        targ = e.target;\n","    else if (e.srcElement)\n","        targ = e.srcElement;\n","    if (targ.nodeType == 3) // defeat Safari bug\n","        targ = targ.parentNode;\n","\n","    // jQuery normalizes the pageX and pageY\n","    // pageX,Y are the mouse positions relative to the document\n","    // offset() returns the position of the element relative to the document\n","    var x = e.pageX - $(targ).offset().left;\n","    var y = e.pageY - $(targ).offset().top;\n","\n","    return {\"x\": x, \"y\": y};\n","};\n","\n","/*\n"," * return a copy of an object with only non-object keys\n"," * we need this to avoid circular references\n"," * http://stackoverflow.com/a/24161582/3208463\n"," */\n","function simpleKeys (original) {\n","  return Object.keys(original).reduce(function (obj, key) {\n","    if (typeof original[key] !== 'object')\n","        obj[key] = original[key]\n","    return obj;\n","  }, {});\n","}\n","\n","mpl.figure.prototype.mouse_event = function(event, name) {\n","    var canvas_pos = mpl.findpos(event)\n","\n","    if (name === 'button_press')\n","    {\n","        this.canvas.focus();\n","        this.canvas_div.focus();\n","    }\n","\n","    var x = canvas_pos.x * mpl.ratio;\n","    var y = canvas_pos.y * mpl.ratio;\n","\n","    this.send_message(name, {x: x, y: y, button: event.button,\n","                             step: event.step,\n","                             guiEvent: simpleKeys(event)});\n","\n","    /* This prevents the web browser from automatically changing to\n","     * the text insertion cursor when the button is pressed.  We want\n","     * to control all of the cursor setting manually through the\n","     * 'cursor' event from matplotlib */\n","    event.preventDefault();\n","    return false;\n","}\n","\n","mpl.figure.prototype._key_event_extra = function(event, name) {\n","    // Handle any extra behaviour associated with a key event\n","}\n","\n","mpl.figure.prototype.key_event = function(event, name) {\n","\n","    // Prevent repeat events\n","    if (name == 'key_press')\n","    {\n","        if (event.which === this._key)\n","            return;\n","        else\n","            this._key = event.which;\n","    }\n","    if (name == 'key_release')\n","        this._key = null;\n","\n","    var value = '';\n","    if (event.ctrlKey && event.which != 17)\n","        value += \"ctrl+\";\n","    if (event.altKey && event.which != 18)\n","        value += \"alt+\";\n","    if (event.shiftKey && event.which != 16)\n","        value += \"shift+\";\n","\n","    value += 'k';\n","    value += event.which.toString();\n","\n","    this._key_event_extra(event, name);\n","\n","    this.send_message(name, {key: value,\n","                             guiEvent: simpleKeys(event)});\n","    return false;\n","}\n","\n","mpl.figure.prototype.toolbar_button_onclick = function(name) {\n","    if (name == 'download') {\n","        this.handle_save(this, null);\n","    } else {\n","        this.send_message(\"toolbar_button\", {name: name});\n","    }\n","};\n","\n","mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n","    this.message.textContent = tooltip;\n","};\n","mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n","\n","mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n","\n","mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n","    // Create a \"websocket\"-like object which calls the given IPython comm\n","    // object with the appropriate methods. Currently this is a non binary\n","    // socket, so there is still some room for performance tuning.\n","    var ws = {};\n","\n","    ws.close = function() {\n","        comm.close()\n","    };\n","    ws.send = function(m) {\n","        //console.log('sending', m);\n","        comm.send(m);\n","    };\n","    // Register the callback with on_msg.\n","    comm.on_msg(function(msg) {\n","        //console.log('receiving', msg['content']['data'], msg);\n","        // Pass the mpl event to the overridden (by mpl) onmessage function.\n","        ws.onmessage(msg['content']['data'])\n","    });\n","    return ws;\n","}\n","\n","mpl.mpl_figure_comm = function(comm, msg) {\n","    // This is the function which gets called when the mpl process\n","    // starts-up an IPython Comm through the \"matplotlib\" channel.\n","\n","    var id = msg.content.data.id;\n","    // Get hold of the div created by the display call when the Comm\n","    // socket was opened in Python.\n","    var element = $(\"#\" + id);\n","    var ws_proxy = comm_websocket_adapter(comm)\n","\n","    function ondownload(figure, format) {\n","        window.open(figure.imageObj.src);\n","    }\n","\n","    var fig = new mpl.figure(id, ws_proxy,\n","                           ondownload,\n","                           element.get(0));\n","\n","    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n","    // web socket which is closed, not our websocket->open comm proxy.\n","    ws_proxy.onopen();\n","\n","    fig.parent_element = element.get(0);\n","    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n","    if (!fig.cell_info) {\n","        console.error(\"Failed to find cell for figure\", id, fig);\n","        return;\n","    }\n","\n","    var output_index = fig.cell_info[2]\n","    var cell = fig.cell_info[0];\n","\n","};\n","\n","mpl.figure.prototype.handle_close = function(fig, msg) {\n","    var width = fig.canvas.width/mpl.ratio\n","    fig.root.unbind('remove')\n","\n","    // Update the output cell to use the data from the current canvas.\n","    fig.push_to_output();\n","    var dataURL = fig.canvas.toDataURL();\n","    // Re-enable the keyboard manager in IPython - without this line, in FF,\n","    // the notebook keyboard shortcuts fail.\n","    IPython.keyboard_manager.enable()\n","    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n","    fig.close_ws(fig, msg);\n","}\n","\n","mpl.figure.prototype.close_ws = function(fig, msg){\n","    fig.send_message('closing', msg);\n","    // fig.ws.close()\n","}\n","\n","mpl.figure.prototype.push_to_output = function(remove_interactive) {\n","    // Turn the data on the canvas into data in the output cell.\n","    var width = this.canvas.width/mpl.ratio\n","    var dataURL = this.canvas.toDataURL();\n","    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n","}\n","\n","mpl.figure.prototype.updated_canvas_event = function() {\n","    // Tell IPython that the notebook contents must change.\n","    IPython.notebook.set_dirty(true);\n","    this.send_message(\"ack\", {});\n","    var fig = this;\n","    // Wait a second, then push the new image to the DOM so\n","    // that it is saved nicely (might be nice to debounce this).\n","    setTimeout(function () { fig.push_to_output() }, 1000);\n","}\n","\n","mpl.figure.prototype._init_toolbar = function() {\n","    var fig = this;\n","\n","    var nav_element = $('<div/>');\n","    nav_element.attr('style', 'width: 100%');\n","    this.root.append(nav_element);\n","\n","    // Define a callback function for later on.\n","    function toolbar_event(event) {\n","        return fig.toolbar_button_onclick(event['data']);\n","    }\n","    function toolbar_mouse_event(event) {\n","        return fig.toolbar_button_onmouseover(event['data']);\n","    }\n","\n","    for(var toolbar_ind in mpl.toolbar_items){\n","        var name = mpl.toolbar_items[toolbar_ind][0];\n","        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n","        var image = mpl.toolbar_items[toolbar_ind][2];\n","        var method_name = mpl.toolbar_items[toolbar_ind][3];\n","\n","        if (!name) { continue; };\n","\n","        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n","        button.click(method_name, toolbar_event);\n","        button.mouseover(tooltip, toolbar_mouse_event);\n","        nav_element.append(button);\n","    }\n","\n","    // Add the status bar.\n","    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n","    nav_element.append(status_bar);\n","    this.message = status_bar[0];\n","\n","    // Add the close button to the window.\n","    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n","    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n","    button.click(function (evt) { fig.handle_close(fig, {}); } );\n","    button.mouseover('Stop Interaction', toolbar_mouse_event);\n","    buttongrp.append(button);\n","    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n","    titlebar.prepend(buttongrp);\n","}\n","\n","mpl.figure.prototype._root_extra_style = function(el){\n","    var fig = this\n","    el.on(\"remove\", function(){\n","\tfig.close_ws(fig, {});\n","    });\n","}\n","\n","mpl.figure.prototype._canvas_extra_style = function(el){\n","    // this is important to make the div 'focusable\n","    el.attr('tabindex', 0)\n","    // reach out to IPython and tell the keyboard manager to turn it's self\n","    // off when our div gets focus\n","\n","    // location in version 3\n","    if (IPython.notebook.keyboard_manager) {\n","        IPython.notebook.keyboard_manager.register_events(el);\n","    }\n","    else {\n","        // location in version 2\n","        IPython.keyboard_manager.register_events(el);\n","    }\n","\n","}\n","\n","mpl.figure.prototype._key_event_extra = function(event, name) {\n","    var manager = IPython.notebook.keyboard_manager;\n","    if (!manager)\n","        manager = IPython.keyboard_manager;\n","\n","    // Check for shift+enter\n","    if (event.shiftKey && event.which == 13) {\n","        this.canvas_div.blur();\n","        // select the cell after this one\n","        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n","        IPython.notebook.select(index + 1);\n","    }\n","}\n","\n","mpl.figure.prototype.handle_save = function(fig, msg) {\n","    fig.ondownload(fig, null);\n","}\n","\n","\n","mpl.find_output_cell = function(html_output) {\n","    // Return the cell and output element which can be found *uniquely* in the notebook.\n","    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n","    // IPython event is triggered only after the cells have been serialised, which for\n","    // our purposes (turning an active figure into a static one), is too late.\n","    var cells = IPython.notebook.get_cells();\n","    var ncells = cells.length;\n","    for (var i=0; i<ncells; i++) {\n","        var cell = cells[i];\n","        if (cell.cell_type === 'code'){\n","            for (var j=0; j<cell.output_area.outputs.length; j++) {\n","                var data = cell.output_area.outputs[j];\n","                if (data.data) {\n","                    // IPython >= 3 moved mimebundle to data attribute of output\n","                    data = data.data;\n","                }\n","                if (data['text/html'] == html_output) {\n","                    return [cell, data, j];\n","                }\n","            }\n","        }\n","    }\n","}\n","\n","// Register the function which deals with the matplotlib target/channel.\n","// The kernel may be null if the page has been refreshed.\n","if (IPython.notebook.kernel != null) {\n","    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n","}\n"],"text/plain":["<IPython.core.display.Javascript object>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["<div id='af388d69-138b-480f-b784-f77bd1e8d66e'></div>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Sensitivity = 98.75%\n","Specificity = 99.69%\n","Classification Accuracy = 98.67%\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fvqWIRhH67zN","executionInfo":{"status":"ok","timestamp":1619608371241,"user_tz":-330,"elapsed":151231,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"f13cdeca-2ff7-4123-95c4-33d01d990650"},"source":["#check output of model\n","y_predict=model.predict(X_test)\n","\n","# define vector\n","probs = asarray(y_predict)\n","print(probs.shape)\n","# get argmax\n","result = argmax(probs, axis=1)\n","print(result)\n","\n","y_test_cm=argmax(y_test,axis=1)\n","print(result.shape)\n","\n","\n","\n","confusion = confusion_matrix(y_test_cm, result)\n","print('Confusion Matrix\\n')\n","print(confusion)\n","\n","#importing accuracy_score, precision_score, recall_score, f1_score\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","print('\\nAccuracy: {:.2f}\\n'.format(accuracy_score(y_test_cm, result)))\n","\n","print('Micro Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='micro')))\n","print('Micro Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='micro')))\n","print('Micro F1-score: {:.2f}\\n'.format(f1_score(y_test_cm, result, average='micro')))\n","\n","print('Macro Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='macro')))\n","print('Macro Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='macro')))\n","print('Macro F1-score: {:.2f}\\n'.format(f1_score(y_test_cm, result, average='macro')))\n","\n","print('Weighted Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='weighted')))\n","print('Weighted Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='weighted')))\n","print('Weighted F1-score: {:.2f}'.format(f1_score(y_test_cm, result, average='weighted')))\n","\n","from sklearn.metrics import classification_report\n","print('\\nClassification Report\\n')\n","print(classification_report(y_test_cm, result, target_names=['Class AB', 'Class CD', 'Class E']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(150, 3)\n","[0 0 0 0 2 1 0 0 2 0 1 2 2 0 2 1 0 1 2 2 0 2 1 0 1 1 1 0 0 2 0 1 0 0 1 1 1\n"," 2 2 1 1 0 1 2 0 2 0 1 1 0 2 2 2 2 2 0 1 1 1 1 0 0 2 1 0 1 1 2 2 0 1 2 1 1\n"," 2 1 1 2 0 0 1 1 0 0 0 1 1 0 1 0 1 1 1 0 0 1 0 1 2 0 0 2 0 1 2 1 1 1 2 0 1\n"," 1 0 0 0 1 0 0 0 2 0 2 0 0 1 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 2 0 0 0\n"," 1 1]\n","(150,)\n","Confusion Matrix\n","\n","[[60  0  0]\n"," [ 1 58  1]\n"," [ 0  0 30]]\n","\n","Accuracy: 0.99\n","\n","Micro Precision: 0.99\n","Micro Recall: 0.99\n","Micro F1-score: 0.99\n","\n","Macro Precision: 0.98\n","Macro Recall: 0.99\n","Macro F1-score: 0.99\n","\n","Weighted Precision: 0.99\n","Weighted Recall: 0.99\n","Weighted F1-score: 0.99\n","\n","Classification Report\n","\n","              precision    recall  f1-score   support\n","\n","    Class AB       0.98      1.00      0.99        60\n","    Class CD       1.00      0.97      0.98        60\n","     Class E       0.97      1.00      0.98        30\n","\n","    accuracy                           0.99       150\n","   macro avg       0.98      0.99      0.99       150\n","weighted avg       0.99      0.99      0.99       150\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"4F8R9igvJ7iB"},"source":["# [A.1] Saving frst model into drive"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SGi8VnI4KupI","executionInfo":{"status":"ok","timestamp":1619609576242,"user_tz":-330,"elapsed":1365,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"3d163af9-97f6-48ed-9783-68837950f294"},"source":["# serialize model to JSON\n","model_json = model.to_json()\n","with open(\"/content/drive/MyDrive/mtech_finalyr_project/3 class problem/model_3class_ABvsCDvsE.json\", \"w\") as json_file:\n","    json_file.write(model_json)\n","# serialize weights to HDF5\n","model.save_weights(\"/content/drive/MyDrive/mtech_finalyr_project/3 class problem/model_3class_ABvsCDvsE.h5\")\n","print(\"Saved model to disk\")\n"," "],"execution_count":null,"outputs":[{"output_type":"stream","text":["Saved model to disk\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ybtJ4y2XKF-X"},"source":["# [A.2] loading frst model from drive"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LEEQQm6wKQ8z","executionInfo":{"status":"ok","timestamp":1619877075673,"user_tz":-330,"elapsed":1394,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"18747a48-e85f-4cf5-e8ac-ed7df487be89"},"source":["from keras.models import model_from_json\n","\n","# load json and create model\n","json_file = open('/content/drive/MyDrive/mtech_finalyr_project/3 class problem/model_3class_ABvsCDvsE.json', 'r')\n","loaded_model_json = json_file.read()\n","json_file.close()\n","loaded_model = model_from_json(loaded_model_json)\n","# load weights into new model\n","loaded_model.load_weights(\"/content/drive/MyDrive/mtech_finalyr_project/3 class problem/model_3class_ABvsCDvsE.h5\")\n","print(\"Loaded model from disk\")\n"],"execution_count":11,"outputs":[{"output_type":"stream","text":["Loaded model from disk\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"vSFEXPMHK7Dk"},"source":["# [A.3] Evaluating Test-dataset(150*4096/2)with frst model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iY-KtyEkLUCt","executionInfo":{"status":"ok","timestamp":1619877175675,"user_tz":-330,"elapsed":33891,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"900ad36f-93b2-4948-a509-14e7101e4b3a"},"source":["#dataset of size 150*(4096/2)\n","print(\"shape of X_test: \",X_test.shape)\n","df_2048=X_test[:,0:2048].copy()\n","print(\"shape of df_2048: \",df_2048.shape)\n","\n","\n","X_test_2048 = pd.concat([pd.DataFrame(df_2048), pd.DataFrame(df_2048)], axis=1,  ignore_index=True)\n","print(\"\\nshape of df_2048: \",df_2048.shape)\n","print(df_2048)\n","\n","X_test_2048=pd.DataFrame.to_numpy(X_test_2048)\n","\n","X_test_2048 = X_test_2048.reshape([X_test_2048.shape[0], timesteps, data_dim])\n","y_test=np_utils.to_categorical(y_test, num_classes=3)\n","\n","\n","\n","# evaluate loaded model on test data\n","loaded_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[sensitivity, specificity, 'accuracy'])\n","score = loaded_model.evaluate(X_test_2048,y_test, verbose=1)\n","print(\" %.2f%%\" % ( score[1]*100))"],"execution_count":15,"outputs":[{"output_type":"stream","text":["shape of X_test:  (150, 4096)\n","shape of df_2048:  (150, 2048)\n","\n","shape of df_2048:  (150, 2048)\n","[[-53. -15.  11. ... -32. -64. -80.]\n"," [-42. -49. -37. ...  32.  23. -14.]\n"," [ 92. 110. 103. ...  48.  21.   5.]\n"," ...\n"," [-53. -33. -23. ... 103. 117. 107.]\n"," [-11.  -9. -11. ... -37. -36. -25.]\n"," [ 24.  19.  14. ... -12.  -8. -15.]]\n","5/5 [==============================] - 33s 77ms/step - loss: 0.0486 - sensitivity: 0.9716 - specificity: 0.9897 - accuracy: 0.9791\n"," 98.12%\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2E6nYPz3MNpi","executionInfo":{"status":"ok","timestamp":1619877217492,"user_tz":-330,"elapsed":1061,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"f86c8cc2-bc95-4f73-e7ae-9689e0a3ab2b"},"source":["score"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.039144616574048996,\n"," 0.981249988079071,\n"," 0.9937499761581421,\n"," 0.9866666793823242]"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"markdown","metadata":{"id":"epVGcCNbNZ4j"},"source":["# [A.4] Evaluating Test-dataset(150*4096/4)with frst model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_nBC2zRtNUot","executionInfo":{"status":"ok","timestamp":1619877550342,"user_tz":-330,"elapsed":2994,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"f84ab4e2-6167-4468-a656-c0650d302dea"},"source":["#dataset of size 150*(1024)\n","print(\"shape of X_test: \",X_test.shape)\n","df_1024=X_test[:,0:1024].copy()\n","print(\"shape of df_1024: \",df_1024.shape)\n","\n","\n","X_test_1024 = pd.concat([pd.DataFrame(df_1024),pd.DataFrame(df_1024),pd.DataFrame(df_1024), pd.DataFrame(df_1024)], axis=1,  ignore_index=True)\n","print(\"\\nshape of df_1024: \",df_1024.shape)\n","print(df_1024)\n","\n","X_test_1024=pd.DataFrame.to_numpy(X_test_1024)\n","\n","X_test_1024 = X_test_1024.reshape([X_test_1024.shape[0], timesteps, data_dim])\n","y_test=np_utils.to_categorical(y_test, num_classes=3)\n","\n","\n","\n","# evaluate loaded model on test data\n","loaded_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[sensitivity, specificity, 'accuracy'])\n","score = loaded_model.evaluate(X_test_1024,y_test, verbose=1)\n","#print(\" %.2f%%\" % ( score[1]*100))"],"execution_count":22,"outputs":[{"output_type":"stream","text":["shape of X_test:  (150, 4096)\n","shape of df_1024:  (150, 1024)\n","\n","shape of df_1024:  (150, 1024)\n","[[-53. -15.  11. ...  68.  87.  80.]\n"," [-42. -49. -37. ... 107.  56.  32.]\n"," [ 92. 110. 103. ... -29. -35. -41.]\n"," ...\n"," [-53. -33. -23. ... -71. -32.  -5.]\n"," [-11.  -9. -11. ... -16. -15. -20.]\n"," [ 24.  19.  14. ...   8.  18.  23.]]\n","5/5 [==============================] - 2s 79ms/step - loss: 0.0588 - sensitivity: 0.9923 - specificity: 0.9961 - accuracy: 0.9921\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"v0EYg_fmOTWa","executionInfo":{"status":"ok","timestamp":1619877572111,"user_tz":-330,"elapsed":1055,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"e76b6214-18d3-43c7-e21a-f9a9bfdd10e9"},"source":["score"],"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.05223961919546127,\n"," 0.9937499761581421,\n"," 0.996874988079071,\n"," 0.9933333396911621]"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"markdown","metadata":{"id":"tL6E_yS4OvfS"},"source":["# [A.5] Evaluating Test-dataset(150*4096/8)with frst model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ML2H8fNAOy4E","executionInfo":{"status":"ok","timestamp":1619877875265,"user_tz":-330,"elapsed":3339,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"d6beea41-48aa-4d05-c33e-f520a8fdc4bd"},"source":["#dataset of size 150*(512)\n","print(\"shape of X_test: \",X_test.shape)\n","df_512=X_test[:,0:512].copy()\n","print(\"shape of df_512: \",df_512.shape)\n","\n","\n","X_test_512 = pd.concat([pd.DataFrame(df_512),pd.DataFrame(df_512),pd.DataFrame(df_512), pd.DataFrame(df_512) , pd.DataFrame(df_512),pd.DataFrame(df_512),pd.DataFrame(df_512), pd.DataFrame(df_512)], axis=1,  ignore_index=True)\n","print(\"\\nshape of df_512: \",df_512.shape)\n","print(df_512)\n","\n","X_test_512=pd.DataFrame.to_numpy(X_test_512)\n","\n","X_test_512 = X_test_512.reshape([X_test_512.shape[0], timesteps, data_dim])\n","#y_test=np_utils.to_categorical(y_test, num_classes=3)\n","\n","\n","\n","# evaluate loaded model on test data\n","loaded_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[sensitivity, specificity, 'accuracy'])\n","score_512 = loaded_model.evaluate(X_test_512,y_test, verbose=1)\n","#print(\" %.2f%%\" % ( score[1]*100))"],"execution_count":29,"outputs":[{"output_type":"stream","text":["shape of X_test:  (150, 4096)\n","shape of df_512:  (150, 512)\n","\n","shape of df_512:  (150, 512)\n","[[-53. -15.  11. ... -75. -80. -69.]\n"," [-42. -49. -37. ... -14.  28.  30.]\n"," [ 92. 110. 103. ...   7.  -1. -34.]\n"," ...\n"," [-53. -33. -23. ...  91.  25. -45.]\n"," [-11.  -9. -11. ... -20. -19. -17.]\n"," [ 24.  19.  14. ... -44. -36. -34.]]\n","5/5 [==============================] - 2s 78ms/step - loss: 0.0852 - sensitivity: 0.9635 - specificity: 0.9817 - accuracy: 0.9637\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SmgTiNVHPYu_","executionInfo":{"status":"ok","timestamp":1619877875268,"user_tz":-330,"elapsed":1000,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"d2f7a4c3-6a1f-4df4-98a7-79abfdebdd4f"},"source":["print(score)\n","print(score_512)"],"execution_count":30,"outputs":[{"output_type":"stream","text":["[0.081826351583004, 0.9659091234207153, 0.9829546213150024, 0.9666666388511658]\n","[0.081826351583004, 0.9659091234207153, 0.9829546213150024, 0.9666666388511658]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"xqbYb0BK7kLW"},"source":["# (B) 3 class classification [A vs C vs E]"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":438},"id":"-Z_jaabg7b6a","executionInfo":{"status":"ok","timestamp":1619609583803,"user_tz":-330,"elapsed":2470,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"67cd6c2d-6dfd-4d6d-cc2c-c87b3a7a17e4"},"source":["df=pd.read_csv(\"/content/drive/My Drive/mtech_finalyr_project/modified_Dataset/final_csv_with_shuffle.csv\")\n","df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>10</th>\n","      <th>11</th>\n","      <th>12</th>\n","      <th>13</th>\n","      <th>14</th>\n","      <th>15</th>\n","      <th>16</th>\n","      <th>17</th>\n","      <th>18</th>\n","      <th>19</th>\n","      <th>20</th>\n","      <th>21</th>\n","      <th>22</th>\n","      <th>23</th>\n","      <th>24</th>\n","      <th>25</th>\n","      <th>26</th>\n","      <th>27</th>\n","      <th>28</th>\n","      <th>29</th>\n","      <th>30</th>\n","      <th>31</th>\n","      <th>32</th>\n","      <th>33</th>\n","      <th>34</th>\n","      <th>35</th>\n","      <th>36</th>\n","      <th>37</th>\n","      <th>38</th>\n","      <th>...</th>\n","      <th>4058</th>\n","      <th>4059</th>\n","      <th>4060</th>\n","      <th>4061</th>\n","      <th>4062</th>\n","      <th>4063</th>\n","      <th>4064</th>\n","      <th>4065</th>\n","      <th>4066</th>\n","      <th>4067</th>\n","      <th>4068</th>\n","      <th>4069</th>\n","      <th>4070</th>\n","      <th>4071</th>\n","      <th>4072</th>\n","      <th>4073</th>\n","      <th>4074</th>\n","      <th>4075</th>\n","      <th>4076</th>\n","      <th>4077</th>\n","      <th>4078</th>\n","      <th>4079</th>\n","      <th>4080</th>\n","      <th>4081</th>\n","      <th>4082</th>\n","      <th>4083</th>\n","      <th>4084</th>\n","      <th>4085</th>\n","      <th>4086</th>\n","      <th>4087</th>\n","      <th>4088</th>\n","      <th>4089</th>\n","      <th>4090</th>\n","      <th>4091</th>\n","      <th>4092</th>\n","      <th>4093</th>\n","      <th>4094</th>\n","      <th>4095</th>\n","      <th>4096</th>\n","      <th>tag</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>-18.0</td>\n","      <td>-55.0</td>\n","      <td>-126.0</td>\n","      <td>-202.0</td>\n","      <td>-238.0</td>\n","      <td>-226.0</td>\n","      <td>-171.0</td>\n","      <td>-111.0</td>\n","      <td>-73.0</td>\n","      <td>-48.0</td>\n","      <td>-45.0</td>\n","      <td>-53.0</td>\n","      <td>-95.0</td>\n","      <td>-165.0</td>\n","      <td>-243.0</td>\n","      <td>-288.0</td>\n","      <td>-309.0</td>\n","      <td>-237.0</td>\n","      <td>-95.0</td>\n","      <td>163.0</td>\n","      <td>528.0</td>\n","      <td>899.0</td>\n","      <td>1179.0</td>\n","      <td>1316.0</td>\n","      <td>1283.0</td>\n","      <td>1132.0</td>\n","      <td>907.0</td>\n","      <td>672.0</td>\n","      <td>338.0</td>\n","      <td>-112.0</td>\n","      <td>-340.0</td>\n","      <td>-430.0</td>\n","      <td>-38.0</td>\n","      <td>293.0</td>\n","      <td>379.0</td>\n","      <td>178.0</td>\n","      <td>-148.0</td>\n","      <td>-375.0</td>\n","      <td>-415.0</td>\n","      <td>...</td>\n","      <td>180.0</td>\n","      <td>-125.0</td>\n","      <td>48.0</td>\n","      <td>204.0</td>\n","      <td>747.0</td>\n","      <td>1153.0</td>\n","      <td>1183.0</td>\n","      <td>949.0</td>\n","      <td>534.0</td>\n","      <td>153.0</td>\n","      <td>-83.0</td>\n","      <td>-168.0</td>\n","      <td>-208.0</td>\n","      <td>-250.0</td>\n","      <td>-246.0</td>\n","      <td>-235.0</td>\n","      <td>-224.0</td>\n","      <td>-244.0</td>\n","      <td>-275.0</td>\n","      <td>-305.0</td>\n","      <td>-334.0</td>\n","      <td>-368.0</td>\n","      <td>-394.0</td>\n","      <td>-406.0</td>\n","      <td>-398.0</td>\n","      <td>-361.0</td>\n","      <td>-309.0</td>\n","      <td>-225.0</td>\n","      <td>-129.0</td>\n","      <td>-59.0</td>\n","      <td>-48.0</td>\n","      <td>-94.0</td>\n","      <td>-161.0</td>\n","      <td>-210.0</td>\n","      <td>-222.0</td>\n","      <td>-224.0</td>\n","      <td>-200.0</td>\n","      <td>-127.0</td>\n","      <td>-226.0</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>-26.0</td>\n","      <td>1.0</td>\n","      <td>29.0</td>\n","      <td>41.0</td>\n","      <td>33.0</td>\n","      <td>2.0</td>\n","      <td>-31.0</td>\n","      <td>-60.0</td>\n","      <td>-81.0</td>\n","      <td>-99.0</td>\n","      <td>-117.0</td>\n","      <td>-140.0</td>\n","      <td>-168.0</td>\n","      <td>-200.0</td>\n","      <td>-242.0</td>\n","      <td>-280.0</td>\n","      <td>-313.0</td>\n","      <td>-339.0</td>\n","      <td>-350.0</td>\n","      <td>-345.0</td>\n","      <td>-323.0</td>\n","      <td>-292.0</td>\n","      <td>-271.0</td>\n","      <td>-269.0</td>\n","      <td>-292.0</td>\n","      <td>-354.0</td>\n","      <td>-487.0</td>\n","      <td>-682.0</td>\n","      <td>-747.0</td>\n","      <td>-635.0</td>\n","      <td>-330.0</td>\n","      <td>5.0</td>\n","      <td>208.0</td>\n","  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<td>260.0</td>\n","      <td>367.0</td>\n","      <td>493.0</td>\n","      <td>566.0</td>\n","      <td>554.0</td>\n","      <td>460.0</td>\n","      <td>343.0</td>\n","      <td>247.0</td>\n","      <td>-468.0</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>343.0</td>\n","      <td>311.0</td>\n","      <td>284.0</td>\n","      <td>274.0</td>\n","      <td>260.0</td>\n","      <td>237.0</td>\n","      <td>165.0</td>\n","      <td>-33.0</td>\n","      <td>-271.0</td>\n","      <td>-425.0</td>\n","      <td>-418.0</td>\n","      <td>-254.0</td>\n","      <td>-104.0</td>\n","      <td>-14.0</td>\n","      <td>16.0</td>\n","      <td>24.0</td>\n","      <td>23.0</td>\n","      <td>11.0</td>\n","      <td>4.0</td>\n","      <td>20.0</td>\n","      <td>40.0</td>\n","      <td>67.0</td>\n","      <td>99.0</td>\n","      <td>127.0</td>\n","      <td>130.0</td>\n","      <td>126.0</td>\n","      <td>133.0</td>\n","      <td>124.0</td>\n","      <td>108.0</td>\n","      <td>54.0</td>\n","      <td>-5.0</td>\n","      <td>-45.0</td>\n","      <td>-61.0</td>\n","      <td>-65.0</td>\n","      <td>-52.0</td>\n","      <td>-46.0</td>\n","      <td>-25.0</td>\n","      <td>-15.0</td>\n","      <td>-4.0</td>\n","      <td>...</td>\n","      <td>-70.0</td>\n","      <td>-118.0</td>\n","      <td>-155.0</td>\n","      <td>-201.0</td>\n","      <td>-283.0</td>\n","      <td>-368.0</td>\n","      <td>-363.0</td>\n","      <td>-333.0</td>\n","      <td>-271.0</td>\n","      <td>-203.0</td>\n","      <td>-126.0</td>\n","      <td>-31.0</td>\n","      <td>48.0</td>\n","      <td>86.0</td>\n","      <td>102.0</td>\n","      <td>124.0</td>\n","      <td>125.0</td>\n","      <td>132.0</td>\n","      <td>144.0</td>\n","      <td>187.0</td>\n","      <td>263.0</td>\n","      <td>343.0</td>\n","      <td>411.0</td>\n","      <td>433.0</td>\n","      <td>417.0</td>\n","      <td>397.0</td>\n","      <td>395.0</td>\n","      <td>414.0</td>\n","      <td>435.0</td>\n","      <td>439.0</td>\n","      <td>428.0</td>\n","      <td>423.0</td>\n","      <td>430.0</td>\n","      <td>472.0</td>\n","      <td>515.0</td>\n","      <td>527.0</td>\n","      <td>480.0</td>\n","      <td>397.0</td>\n","      <td>217.0</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>-63.0</td>\n","      <td>-107.0</td>\n","      <td>-208.0</td>\n","      <td>-310.0</td>\n","      <td>-395.0</td>\n","      <td>-491.0</td>\n","      <td>-463.0</td>\n","      <td>-426.0</td>\n","      <td>-275.0</td>\n","      <td>-114.0</td>\n","      <td>34.0</td>\n","      <td>142.0</td>\n","      <td>191.0</td>\n","      <td>183.0</td>\n","      <td>141.0</td>\n","      <td>103.0</td>\n","      <td>76.0</td>\n","      <td>66.0</td>\n","      <td>74.0</td>\n","      <td>95.0</td>\n","      <td>110.0</td>\n","      <td>127.0</td>\n","      <td>136.0</td>\n","      <td>141.0</td>\n","      <td>141.0</td>\n","      <td>137.0</td>\n","      <td>118.0</td>\n","      <td>89.0</td>\n","      <td>42.0</td>\n","      <td>6.0</td>\n","      <td>-33.0</td>\n","      <td>-52.0</td>\n","      <td>-49.0</td>\n","      <td>-31.0</td>\n","      <td>-5.0</td>\n","      <td>23.0</td>\n","      <td>40.0</td>\n","      <td>47.0</td>\n","      <td>45.0</td>\n","      <td>...</td>\n","      <td>-588.0</td>\n","      <td>-534.0</td>\n","      <td>-475.0</td>\n","      <td>-405.0</td>\n","      <td>-330.0</td>\n","      <td>-281.0</td>\n","      <td>-269.0</td>\n","      <td>-296.0</td>\n","      <td>-358.0</td>\n","      <td>-393.0</td>\n","      <td>-363.0</td>\n","      <td>-251.0</td>\n","      <td>-131.0</td>\n","      <td>-57.0</td>\n","      <td>-81.0</td>\n","      <td>-156.0</td>\n","      <td>-239.0</td>\n","      <td>-318.0</td>\n","      <td>-396.0</td>\n","      <td>-372.0</td>\n","      <td>-270.0</td>\n","      <td>-71.0</td>\n","      <td>66.0</td>\n","      <td>75.0</td>\n"," 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     <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>495</th>\n","      <td>495</td>\n","      <td>213.0</td>\n","      <td>210.0</td>\n","      <td>210.0</td>\n","      <td>212.0</td>\n","      <td>194.0</td>\n","      <td>162.0</td>\n","      <td>121.0</td>\n","      <td>84.0</td>\n","      <td>45.0</td>\n","      <td>11.0</td>\n","      <td>-13.0</td>\n","      <td>-20.0</td>\n","      <td>-27.0</td>\n","      <td>-32.0</td>\n","      <td>-31.0</td>\n","      <td>-39.0</td>\n","      <td>-49.0</td>\n","      <td>-61.0</td>\n","      <td>-72.0</td>\n","      <td>-80.0</td>\n","      <td>-85.0</td>\n","      <td>-87.0</td>\n","      <td>-93.0</td>\n","      <td>-96.0</td>\n","      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<td>-15.0</td>\n","      <td>-13.0</td>\n","      <td>-14.0</td>\n","      <td>-9.0</td>\n","      <td>-10.0</td>\n","      <td>-5.0</td>\n","      <td>-2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>-2.0</td>\n","      <td>-8.0</td>\n","      <td>-9.0</td>\n","      <td>-2.0</td>\n","      <td>20.0</td>\n","      <td>-231.0</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>496</th>\n","      <td>496</td>\n","      <td>24.0</td>\n","      <td>10.0</td>\n","      <td>-9.0</td>\n","      <td>-18.0</td>\n","      <td>-9.0</td>\n","      <td>-3.0</td>\n","      <td>-2.0</td>\n","      <td>-2.0</td>\n","      <td>-9.0</td>\n","      <td>-9.0</td>\n","      <td>-14.0</td>\n","      <td>-14.0</td>\n","      <td>-24.0</td>\n","      <td>-24.0</td>\n","      <td>-12.0</td>\n","      <td>-1.0</td>\n","      <td>13.0</td>\n","      <td>31.0</td>\n","      <td>60.0</td>\n","      <td>79.0</td>\n","      <td>98.0</td>\n","      <td>110.0</td>\n","      <td>104.0</td>\n","      <td>72.0</td>\n","      <td>40.0</td>\n","      <td>26.0</td>\n","      <td>24.0</td>\n","      <td>33.0</td>\n","      <td>40.0</td>\n","      <td>29.0</td>\n","      <td>13.0</td>\n","      <td>3.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>10.0</td>\n","      <td>32.0</td>\n","      <td>52.0</td>\n","      <td>61.0</td>\n","      <td>54.0</td>\n","      <td>...</td>\n","      <td>55.0</td>\n","      <td>33.0</td>\n","      <td>15.0</td>\n","      <td>-5.0</td>\n","      <td>-18.0</td>\n","      <td>-24.0</td>\n","      <td>-28.0</td>\n","      <td>-15.0</td>\n","      <td>12.0</td>\n","      <td>33.0</td>\n","      <td>48.0</td>\n","      <td>47.0</td>\n","      <td>36.0</td>\n","      <td>19.0</td>\n","      <td>16.0</td>\n","      <td>31.0</td>\n","      <td>56.0</td>\n","      <td>69.0</td>\n","      <td>75.0</td>\n","      <td>67.0</td>\n","      <td>66.0</td>\n","      <td>76.0</td>\n","      <td>93.0</td>\n","      <td>88.0</td>\n","      <td>65.0</td>\n","      <td>56.0</td>\n","      <td>58.0</td>\n","      <td>63.0</td>\n","      <td>48.0</td>\n","      <td>29.0</td>\n","      <td>3.0</td>\n","      <td>-17.0</td>\n","      <td>-14.0</td>\n","      <td>-21.0</td>\n","      <td>-29.0</td>\n","      <td>-39.0</td>\n","      <td>-33.0</td>\n","      <td>-28.0</td>\n","      <td>-19.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>497</th>\n","      <td>497</td>\n","      <td>-20.0</td>\n","      <td>-12.0</td>\n","      <td>-6.0</td>\n","      <td>6.0</td>\n","      <td>8.0</td>\n","      <td>-2.0</td>\n","      <td>-22.0</td>\n","      <td>-39.0</td>\n","      <td>-39.0</td>\n","      <td>-49.0</td>\n","      <td>-46.0</td>\n","      <td>-43.0</td>\n","      <td>-43.0</td>\n","      <td>-46.0</td>\n","      <td>-46.0</td>\n","      <td>-35.0</td>\n","      <td>-23.0</td>\n","      <td>-13.0</td>\n","      <td>-14.0</td>\n","      <td>-17.0</td>\n","      <td>-23.0</td>\n","      <td>-25.0</td>\n","      <td>-18.0</td>\n","      <td>-18.0</td>\n","      <td>-14.0</td>\n","      <td>-16.0</td>\n","      <td>-20.0</td>\n","      <td>-36.0</td>\n","      <td>-46.0</td>\n","      <td>-54.0</td>\n","      <td>-64.0</td>\n","      <td>-64.0</td>\n","      <td>-63.0</td>\n","      <td>-55.0</td>\n","      <td>-53.0</td>\n","      <td>-44.0</td>\n","      <td>-43.0</td>\n","      <td>-47.0</td>\n","      <td>-44.0</td>\n","      <td>...</td>\n","      <td>26.0</td>\n","      <td>20.0</td>\n","      <td>21.0</td>\n","      <td>27.0</td>\n","      <td>33.0</td>\n","      <td>34.0</td>\n","      <td>42.0</td>\n","      <td>49.0</td>\n","      <td>37.0</td>\n","      <td>18.0</td>\n","      <td>-1.0</td>\n","      <td>-18.0</td>\n","      <td>-25.0</td>\n","      <td>-16.0</td>\n","      <td>6.0</td>\n","      <td>23.0</td>\n","      <td>37.0</td>\n","      <td>44.0</td>\n","      <td>39.0</td>\n","      <td>26.0</td>\n","      <td>14.0</td>\n","      <td>13.0</td>\n","      <td>11.0</td>\n","      <td>25.0</td>\n","      <td>38.0</td>\n","      <td>42.0</td>\n","      <td>31.0</td>\n","      <td>16.0</td>\n","      <td>8.0</td>\n","      <td>1.0</td>\n","      <td>7.0</td>\n","      <td>20.0</td>\n","      <td>19.0</td>\n","      <td>11.0</td>\n","      <td>10.0</td>\n","      <td>12.0</td>\n","      <td>9.0</td>\n","      <td>2.0</td>\n","      <td>-14.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>498</th>\n","      <td>498</td>\n","      <td>-104.0</td>\n","      <td>-100.0</td>\n","      <td>-94.0</td>\n","      <td>-87.0</td>\n","      <td>-87.0</td>\n","      <td>-87.0</td>\n","      <td>-88.0</td>\n","      <td>-90.0</td>\n","      <td>-83.0</td>\n","      <td>-75.0</td>\n","      <td>-69.0</td>\n","      <td>-59.0</td>\n","      <td>-50.0</td>\n","      <td>-38.0</td>\n","      <td>-42.0</td>\n","      <td>-50.0</td>\n","      <td>-67.0</td>\n","      <td>-84.0</td>\n","      <td>-92.0</td>\n","      <td>-96.0</td>\n","  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<td>-35.0</td>\n","      <td>-33.0</td>\n","      <td>-31.0</td>\n","      <td>-24.0</td>\n","      <td>-11.0</td>\n","      <td>1.0</td>\n","      <td>8.0</td>\n","      <td>6.0</td>\n","      <td>2.0</td>\n","      <td>-8.0</td>\n","      <td>-16.0</td>\n","      <td>-27.0</td>\n","      <td>-38.0</td>\n","      <td>-45.0</td>\n","      <td>-40.0</td>\n","      <td>-30.0</td>\n","      <td>-37.0</td>\n","      <td>-45.0</td>\n","      <td>-59.0</td>\n","      <td>-53.0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>499</th>\n","      <td>499</td>\n","      <td>-43.0</td>\n","      <td>-57.0</td>\n","      <td>-86.0</td>\n","      <td>-106.0</td>\n","      <td>-106.0</td>\n","      <td>-120.0</td>\n","      <td>-111.0</td>\n","      <td>-88.0</td>\n","      <td>-81.0</td>\n","      <td>-90.0</td>\n","      <td>-109.0</td>\n","      <td>-102.0</td>\n","      <td>-69.0</td>\n","      <td>-20.0</td>\n","      <td>1.0</td>\n","      <td>25.0</td>\n","      <td>42.0</td>\n","      <td>68.0</td>\n","      <td>61.0</td>\n","      <td>36.0</td>\n","      <td>4.0</td>\n","      <td>4.0</td>\n","      <td>9.0</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>-2.0</td>\n","      <td>-13.0</td>\n","      <td>-23.0</td>\n","      <td>-30.0</td>\n","      <td>-39.0</td>\n","      <td>-13.0</td>\n","      <td>29.0</td>\n","      <td>48.0</td>\n","      <td>63.0</td>\n","      <td>67.0</td>\n","      <td>90.0</td>\n","      <td>96.0</td>\n","      <td>84.0</td>\n","      <td>81.0</td>\n","      <td>...</td>\n","      <td>-117.0</td>\n","      <td>-130.0</td>\n","      <td>-132.0</td>\n","      <td>-134.0</td>\n","      <td>-150.0</td>\n","      <td>-161.0</td>\n","      <td>-191.0</td>\n","      <td>-180.0</td>\n","      <td>-145.0</td>\n","      <td>-135.0</td>\n","      <td>-123.0</td>\n","      <td>-139.0</td>\n","      <td>-127.0</td>\n","      <td>-103.0</td>\n","      <td>-111.0</td>\n","      <td>-95.0</td>\n","      <td>-82.0</td>\n","      <td>-55.0</td>\n","      <td>-19.0</td>\n","      <td>-11.0</td>\n","      <td>4.0</td>\n","      <td>16.0</td>\n","      <td>-22.0</td>\n","      <td>-1.0</td>\n","      <td>-20.0</td>\n","      <td>-11.0</td>\n","      <td>6.0</td>\n","      <td>17.0</td>\n","      <td>22.0</td>\n","      <td>3.0</td>\n","      <td>-9.0</td>\n","      <td>6.0</td>\n","      <td>39.0</td>\n","      <td>59.0</td>\n","      <td>80.0</td>\n","      <td>39.0</td>\n","      <td>21.0</td>\n","      <td>-8.0</td>\n","      <td>-43.0</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>500 rows × 4099 columns</p>\n","</div>"],"text/plain":["     Unnamed: 0      0      1      2      3  ...   4093   4094   4095   4096  tag\n","0             0  -18.0  -55.0 -126.0 -202.0  ... -224.0 -200.0 -127.0 -226.0    5\n","1             1  -26.0    1.0   29.0   41.0  ... -209.0 -207.0 -210.0 -107.0    5\n","2             2   68.0 -106.0 -149.0 -141.0  ...  460.0  343.0  247.0 -468.0    5\n","3             3  343.0  311.0  284.0  274.0  ...  527.0  480.0  397.0  217.0    5\n","4             4  -63.0 -107.0 -208.0 -310.0  ...   56.0   44.0  -37.0   28.0    5\n","..          ...    ...    ...    ...    ...  ...    ...    ...    ...    ...  ...\n","495         495  213.0  210.0  210.0  212.0  ...   -9.0   -2.0   20.0 -231.0    5\n","496         496   24.0   10.0   -9.0  -18.0  ...  -39.0  -33.0  -28.0  -19.0    1\n","497         497  -20.0  -12.0   -6.0    6.0  ...   12.0    9.0    2.0  -14.0    1\n","498         498 -104.0 -100.0  -94.0  -87.0  ...  -37.0  -45.0  -59.0  -53.0    3\n","499         499  -43.0  -57.0  -86.0 -106.0  ...   39.0   21.0   -8.0  -43.0    3\n","\n","[500 rows x 4099 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":506},"id":"2o5NS8jm7-sW","executionInfo":{"status":"ok","timestamp":1619609583808,"user_tz":-330,"elapsed":1145,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"d30d41e1-d973-4422-cbb9-d8100771db3e"},"source":["print(df.columns)\n","df=df.drop(['Unnamed: 0'], axis = 1)\n","df=df.drop(['4096'], axis = 1)\n","\n","#select only those record having 0 2 4 tags\n","df=df.loc[(df.tag== 1) | (df.tag == 3) | (df.tag == 5) ]\n","df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8',\n","       ...\n","       '4088', '4089', '4090', '4091', '4092', '4093', '4094', '4095', '4096',\n","       'tag'],\n","      dtype='object', length=4099)\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>10</th>\n","      <th>11</th>\n","      <th>12</th>\n","      <th>13</th>\n","      <th>14</th>\n","      <th>15</th>\n","      <th>16</th>\n","      <th>17</th>\n","      <th>18</th>\n","      <th>19</th>\n","      <th>20</th>\n","      <th>21</th>\n","      <th>22</th>\n","      <th>23</th>\n","      <th>24</th>\n","      <th>25</th>\n","      <th>26</th>\n","      <th>27</th>\n","      <th>28</th>\n","      <th>29</th>\n","      <th>30</th>\n","      <th>31</th>\n","      <th>32</th>\n","      <th>33</th>\n","      <th>34</th>\n","      <th>35</th>\n","      <th>36</th>\n","      <th>37</th>\n","      <th>38</th>\n","      <th>39</th>\n","      <th>...</th>\n","      <th>4057</th>\n","      <th>4058</th>\n","      <th>4059</th>\n","      <th>4060</th>\n","      <th>4061</th>\n","      <th>4062</th>\n","      <th>4063</th>\n","      <th>4064</th>\n","      <th>4065</th>\n","      <th>4066</th>\n","      <th>4067</th>\n","      <th>4068</th>\n","      <th>4069</th>\n","      <th>4070</th>\n","      <th>4071</th>\n","      <th>4072</th>\n","      <th>4073</th>\n","      <th>4074</th>\n","      <th>4075</th>\n","      <th>4076</th>\n","      <th>4077</th>\n","      <th>4078</th>\n","      <th>4079</th>\n","      <th>4080</th>\n","      <th>4081</th>\n","      <th>4082</th>\n","      <th>4083</th>\n","      <th>4084</th>\n","      <th>4085</th>\n","      <th>4086</th>\n","      <th>4087</th>\n","      <th>4088</th>\n","      <th>4089</th>\n","      <th>4090</th>\n","      <th>4091</th>\n","      <th>4092</th>\n","      <th>4093</th>\n","      <th>4094</th>\n","      <th>4095</th>\n","      <th>tag</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>-18.0</td>\n","      <td>-55.0</td>\n","      <td>-126.0</td>\n","      <td>-202.0</td>\n","      <td>-238.0</td>\n","      <td>-226.0</td>\n","      <td>-171.0</td>\n","      <td>-111.0</td>\n","      <td>-73.0</td>\n","      <td>-48.0</td>\n","      <td>-45.0</td>\n","      <td>-53.0</td>\n","      <td>-95.0</td>\n","      <td>-165.0</td>\n","      <td>-243.0</td>\n","      <td>-288.0</td>\n","      <td>-309.0</td>\n","      <td>-237.0</td>\n","      <td>-95.0</td>\n","      <td>163.0</td>\n","      <td>528.0</td>\n","      <td>899.0</td>\n","      <td>1179.0</td>\n","      <td>1316.0</td>\n","      <td>1283.0</td>\n","      <td>1132.0</td>\n","      <td>907.0</td>\n","      <td>672.0</td>\n","      <td>338.0</td>\n","      <td>-112.0</td>\n","      <td>-340.0</td>\n","      <td>-430.0</td>\n","      <td>-38.0</td>\n","      <td>293.0</td>\n","      <td>379.0</td>\n","      <td>178.0</td>\n","      <td>-148.0</td>\n","      <td>-375.0</td>\n","      <td>-415.0</td>\n","      <td>-399.0</td>\n","      <td>...</td>\n","      <td>230.0</td>\n","      <td>180.0</td>\n","      <td>-125.0</td>\n","      <td>48.0</td>\n","      <td>204.0</td>\n","      <td>747.0</td>\n","      <td>1153.0</td>\n","      <td>1183.0</td>\n","      <td>949.0</td>\n","      <td>534.0</td>\n","      <td>153.0</td>\n","      <td>-83.0</td>\n","      <td>-168.0</td>\n","      <td>-208.0</td>\n","      <td>-250.0</td>\n","      <td>-246.0</td>\n","      <td>-235.0</td>\n","      <td>-224.0</td>\n","      <td>-244.0</td>\n","      <td>-275.0</td>\n","      <td>-305.0</td>\n","      <td>-334.0</td>\n","      <td>-368.0</td>\n","      <td>-394.0</td>\n","      <td>-406.0</td>\n","      <td>-398.0</td>\n","      <td>-361.0</td>\n","      <td>-309.0</td>\n","      <td>-225.0</td>\n","      <td>-129.0</td>\n","      <td>-59.0</td>\n","      <td>-48.0</td>\n","      <td>-94.0</td>\n","      <td>-161.0</td>\n","      <td>-210.0</td>\n","      <td>-222.0</td>\n","      <td>-224.0</td>\n","      <td>-200.0</td>\n","      <td>-127.0</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-26.0</td>\n","      <td>1.0</td>\n","      <td>29.0</td>\n","      <td>41.0</td>\n","      <td>33.0</td>\n","      <td>2.0</td>\n","      <td>-31.0</td>\n","      <td>-60.0</td>\n","      <td>-81.0</td>\n","      <td>-99.0</td>\n","      <td>-117.0</td>\n","      <td>-140.0</td>\n","      <td>-168.0</td>\n","      <td>-200.0</td>\n","      <td>-242.0</td>\n","      <td>-280.0</td>\n","      <td>-313.0</td>\n","      <td>-339.0</td>\n","      <td>-350.0</td>\n","      <td>-345.0</td>\n","      <td>-323.0</td>\n","      <td>-292.0</td>\n","      <td>-271.0</td>\n","      <td>-269.0</td>\n","      <td>-292.0</td>\n","      <td>-354.0</td>\n","      <td>-487.0</td>\n","      <td>-682.0</td>\n","      <td>-747.0</td>\n","      <td>-635.0</td>\n","      <td>-330.0</td>\n","      <td>5.0</td>\n","      <td>208.0</td>\n","      <td>313.0</td>\n","      <td>335.0</td>\n","      <td>348.0</td>\n","      <td>358.0</td>\n","      <td>423.0</td>\n","      <td>474.0</td>\n","      <td>456.0</td>\n","      <td>...</td>\n","      <td>-411.0</td>\n","      <td>-423.0</td>\n","      <td>-366.0</td>\n","      <td>-229.0</td>\n","      <td>-79.0</td>\n","      <td>40.0</td>\n","      <td>126.0</td>\n","      <td>195.0</td>\n","      <td>242.0</td>\n","      <td>307.0</td>\n","      <td>395.0</td>\n","      <td>504.0</td>\n","      <td>592.0</td>\n","      <td>656.0</td>\n","      <td>696.0</td>\n","      <td>701.0</td>\n","      <td>659.0</td>\n","      <td>545.0</td>\n","      <td>368.0</td>\n","      <td>196.0</td>\n","      <td>80.0</td>\n","      <td>48.0</td>\n","      <td>73.0</td>\n","      <td>93.0</td>\n","      <td>75.0</td>\n","      <td>5.0</td>\n","      <td>-101.0</td>\n","      <td>-204.0</td>\n","      <td>-279.0</td>\n","      <td>-308.0</td>\n","      <td>-288.0</td>\n","      <td>-254.0</td>\n","      <td>-220.0</td>\n","      <td>-200.0</td>\n","      <td>-201.0</td>\n","      <td>-205.0</td>\n","      <td>-209.0</td>\n","      <td>-207.0</td>\n","      <td>-210.0</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>68.0</td>\n","      <td>-106.0</td>\n","      <td>-149.0</td>\n","      <td>-141.0</td>\n","      <td>-109.0</td>\n","      <td>-69.0</td>\n","      <td>-74.0</td>\n","      <td>-93.0</td>\n","      <td>-110.0</td>\n","      <td>-103.0</td>\n","      <td>-89.0</td>\n","      <td>-72.0</td>\n","      <td>-42.0</td>\n","  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<td>-30.0</td>\n","      <td>-108.0</td>\n","      <td>-183.0</td>\n","      <td>-221.0</td>\n","      <td>-182.0</td>\n","      <td>-92.0</td>\n","      <td>49.0</td>\n","      <td>208.0</td>\n","      <td>299.0</td>\n","      <td>330.0</td>\n","      <td>284.0</td>\n","      <td>203.0</td>\n","      <td>135.0</td>\n","      <td>81.0</td>\n","      <td>57.0</td>\n","      <td>72.0</td>\n","      <td>105.0</td>\n","      <td>146.0</td>\n","      <td>196.0</td>\n","      <td>260.0</td>\n","      <td>367.0</td>\n","      <td>493.0</td>\n","      <td>566.0</td>\n","      <td>554.0</td>\n","      <td>460.0</td>\n","      <td>343.0</td>\n","      <td>247.0</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>343.0</td>\n","      <td>311.0</td>\n","      <td>284.0</td>\n","      <td>274.0</td>\n","      <td>260.0</td>\n","      <td>237.0</td>\n","      <td>165.0</td>\n","      <td>-33.0</td>\n","      <td>-271.0</td>\n","      <td>-425.0</td>\n","      <td>-418.0</td>\n","      <td>-254.0</td>\n","      <td>-104.0</td>\n","      <td>-14.0</td>\n","      <td>16.0</td>\n","      <td>24.0</td>\n","      <td>23.0</td>\n","      <td>11.0</td>\n","      <td>4.0</td>\n","      <td>20.0</td>\n","      <td>40.0</td>\n","      <td>67.0</td>\n","      <td>99.0</td>\n","      <td>127.0</td>\n","      <td>130.0</td>\n","      <td>126.0</td>\n","      <td>133.0</td>\n","      <td>124.0</td>\n","      <td>108.0</td>\n","      <td>54.0</td>\n","      <td>-5.0</td>\n","      <td>-45.0</td>\n","      <td>-61.0</td>\n","      <td>-65.0</td>\n","      <td>-52.0</td>\n","      <td>-46.0</td>\n","      <td>-25.0</td>\n","      <td>-15.0</td>\n","      <td>-4.0</td>\n","      <td>-12.0</td>\n","      <td>...</td>\n","      <td>2.0</td>\n","      <td>-70.0</td>\n","      <td>-118.0</td>\n","      <td>-155.0</td>\n","      <td>-201.0</td>\n","      <td>-283.0</td>\n","      <td>-368.0</td>\n","      <td>-363.0</td>\n","      <td>-333.0</td>\n","      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<td>...</td>\n","      <td>-28.0</td>\n","      <td>-22.0</td>\n","      <td>-23.0</td>\n","      <td>-19.0</td>\n","      <td>-20.0</td>\n","      <td>-18.0</td>\n","      <td>-14.0</td>\n","      <td>-23.0</td>\n","      <td>-37.0</td>\n","      <td>-56.0</td>\n","      <td>-64.0</td>\n","      <td>-67.0</td>\n","      <td>-64.0</td>\n","      <td>-49.0</td>\n","      <td>-40.0</td>\n","      <td>-34.0</td>\n","      <td>-39.0</td>\n","      <td>-39.0</td>\n","      <td>-43.0</td>\n","      <td>-43.0</td>\n","      <td>-35.0</td>\n","      <td>-33.0</td>\n","      <td>-31.0</td>\n","      <td>-24.0</td>\n","      <td>-11.0</td>\n","      <td>1.0</td>\n","      <td>8.0</td>\n","      <td>6.0</td>\n","      <td>2.0</td>\n","      <td>-8.0</td>\n","      <td>-16.0</td>\n","      <td>-27.0</td>\n","      <td>-38.0</td>\n","      <td>-45.0</td>\n","      <td>-40.0</td>\n","      <td>-30.0</td>\n","      <td>-37.0</td>\n","      <td>-45.0</td>\n","      <td>-59.0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>499</th>\n","      <td>-43.0</td>\n","      <td>-57.0</td>\n","      <td>-86.0</td>\n","      <td>-106.0</td>\n","      <td>-106.0</td>\n","      <td>-120.0</td>\n","      <td>-111.0</td>\n","      <td>-88.0</td>\n","      <td>-81.0</td>\n","      <td>-90.0</td>\n","      <td>-109.0</td>\n","      <td>-102.0</td>\n","      <td>-69.0</td>\n","      <td>-20.0</td>\n","      <td>1.0</td>\n","      <td>25.0</td>\n","      <td>42.0</td>\n","      <td>68.0</td>\n","      <td>61.0</td>\n","      <td>36.0</td>\n","      <td>4.0</td>\n","      <td>4.0</td>\n","      <td>9.0</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>-2.0</td>\n","      <td>-13.0</td>\n","      <td>-23.0</td>\n","      <td>-30.0</td>\n","      <td>-39.0</td>\n","      <td>-13.0</td>\n","      <td>29.0</td>\n","      <td>48.0</td>\n","      <td>63.0</td>\n","      <td>67.0</td>\n","      <td>90.0</td>\n","      <td>96.0</td>\n","      <td>84.0</td>\n","      <td>81.0</td>\n","      <td>76.0</td>\n","      <td>...</td>\n","      <td>-129.0</td>\n","      <td>-117.0</td>\n","      <td>-130.0</td>\n","      <td>-132.0</td>\n","      <td>-134.0</td>\n","      <td>-150.0</td>\n","      <td>-161.0</td>\n","      <td>-191.0</td>\n","      <td>-180.0</td>\n","      <td>-145.0</td>\n","      <td>-135.0</td>\n","      <td>-123.0</td>\n","      <td>-139.0</td>\n","      <td>-127.0</td>\n","      <td>-103.0</td>\n","      <td>-111.0</td>\n","      <td>-95.0</td>\n","      <td>-82.0</td>\n","      <td>-55.0</td>\n","      <td>-19.0</td>\n","      <td>-11.0</td>\n","      <td>4.0</td>\n","      <td>16.0</td>\n","      <td>-22.0</td>\n","      <td>-1.0</td>\n","      <td>-20.0</td>\n","      <td>-11.0</td>\n","      <td>6.0</td>\n","      <td>17.0</td>\n","      <td>22.0</td>\n","      <td>3.0</td>\n","      <td>-9.0</td>\n","      <td>6.0</td>\n","      <td>39.0</td>\n","      <td>59.0</td>\n","      <td>80.0</td>\n","      <td>39.0</td>\n","      <td>21.0</td>\n","      <td>-8.0</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>300 rows × 4097 columns</p>\n","</div>"],"text/plain":["         0      1      2      3      4  ...   4092   4093   4094   4095  tag\n","0    -18.0  -55.0 -126.0 -202.0 -238.0  ... -222.0 -224.0 -200.0 -127.0    5\n","1    -26.0    1.0   29.0   41.0   33.0  ... -205.0 -209.0 -207.0 -210.0    5\n","2     68.0 -106.0 -149.0 -141.0 -109.0  ...  554.0  460.0  343.0  247.0    5\n","3    343.0  311.0  284.0  274.0  260.0  ...  515.0  527.0  480.0  397.0    5\n","4    -63.0 -107.0 -208.0 -310.0 -395.0  ...  -32.0   56.0   44.0  -37.0    5\n","..     ...    ...    ...    ...    ...  ...    ...    ...    ...    ...  ...\n","495  213.0  210.0  210.0  212.0  194.0  ...   -8.0   -9.0   -2.0   20.0    5\n","496   24.0   10.0   -9.0  -18.0   -9.0  ...  -29.0  -39.0  -33.0  -28.0    1\n","497  -20.0  -12.0   -6.0    6.0    8.0  ...   10.0   12.0    9.0    2.0    1\n","498 -104.0 -100.0  -94.0  -87.0  -87.0  ...  -30.0  -37.0  -45.0  -59.0    3\n","499  -43.0  -57.0  -86.0 -106.0 -106.0  ...   80.0   39.0   21.0   -8.0    3\n","\n","[300 rows x 4097 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y5I_OnhRAsPz","executionInfo":{"status":"ok","timestamp":1619609587780,"user_tz":-330,"elapsed":1068,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"dba0185a-5ea7-4792-8732-ab758fd6e261"},"source":["print(df.shape)\n","df[\"tag\"].value_counts()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(300, 4097)\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["5    100\n","3    100\n","1    100\n","Name: tag, dtype: int64"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":422},"id":"EtK-i7R37b32","executionInfo":{"status":"ok","timestamp":1619609621712,"user_tz":-330,"elapsed":1119,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"41775c33-7f41-4873-83c3-2ec4613a94e4"},"source":["df.tag=df.tag.replace({ 1:0 , 3:1 , 5:2 })\n","df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr 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<td>-97.0</td>\n","      <td>-94.0</td>\n","      <td>-82.0</td>\n","      <td>-57.0</td>\n","      <td>-18.0</td>\n","      <td>-3.0</td>\n","      <td>-11.0</td>\n","      <td>-1.0</td>\n","      <td>41.0</td>\n","      <td>120.0</td>\n","      <td>181.0</td>\n","      <td>202.0</td>\n","      <td>198.0</td>\n","      <td>195.0</td>\n","      <td>197.0</td>\n","      <td>190.0</td>\n","      <td>...</td>\n","      <td>-110.0</td>\n","      <td>-108.0</td>\n","      <td>-112.0</td>\n","      <td>-113.0</td>\n","      <td>-112.0</td>\n","      <td>-103.0</td>\n","      <td>-100.0</td>\n","      <td>-98.0</td>\n","      <td>-95.0</td>\n","      <td>-94.0</td>\n","      <td>-92.0</td>\n","      <td>-91.0</td>\n","      <td>-89.0</td>\n","      <td>-86.0</td>\n","      <td>-84.0</td>\n","      <td>-78.0</td>\n","      <td>-70.0</td>\n","      <td>-63.0</td>\n","      <td>-53.0</td>\n","      <td>-46.0</td>\n","      <td>-37.0</td>\n","      <td>-34.0</td>\n","      <td>-29.0</td>\n","      <td>-28.0</td>\n","      <td>-23.0</td>\n","      <td>-15.0</td>\n","      <td>-13.0</td>\n","      <td>-14.0</td>\n","      <td>-9.0</td>\n","      <td>-10.0</td>\n","      <td>-5.0</td>\n","      <td>-2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>-2.0</td>\n","      <td>-8.0</td>\n","      <td>-9.0</td>\n","      <td>-2.0</td>\n","      <td>20.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>496</th>\n","      <td>24.0</td>\n","      <td>10.0</td>\n","      <td>-9.0</td>\n","      <td>-18.0</td>\n","      <td>-9.0</td>\n","      <td>-3.0</td>\n","      <td>-2.0</td>\n","      <td>-2.0</td>\n","      <td>-9.0</td>\n","      <td>-9.0</td>\n","      <td>-14.0</td>\n","      <td>-14.0</td>\n","      <td>-24.0</td>\n","      <td>-24.0</td>\n","      <td>-12.0</td>\n","      <td>-1.0</td>\n","      <td>13.0</td>\n","      <td>31.0</td>\n","      <td>60.0</td>\n","      <td>79.0</td>\n","      <td>98.0</td>\n","      <td>110.0</td>\n","      <td>104.0</td>\n","      <td>72.0</td>\n","      <td>40.0</td>\n","      <td>26.0</td>\n","      <td>24.0</td>\n","      <td>33.0</td>\n","      <td>40.0</td>\n","      <td>29.0</td>\n","      <td>13.0</td>\n","      <td>3.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>10.0</td>\n","      <td>32.0</td>\n","      <td>52.0</td>\n","      <td>61.0</td>\n","      <td>54.0</td>\n","      <td>19.0</td>\n","      <td>...</td>\n","      <td>73.0</td>\n","      <td>55.0</td>\n","      <td>33.0</td>\n","      <td>15.0</td>\n","      <td>-5.0</td>\n","      <td>-18.0</td>\n","      <td>-24.0</td>\n","      <td>-28.0</td>\n","      <td>-15.0</td>\n","      <td>12.0</td>\n","      <td>33.0</td>\n","      <td>48.0</td>\n","      <td>47.0</td>\n","      <td>36.0</td>\n","      <td>19.0</td>\n","      <td>16.0</td>\n","      <td>31.0</td>\n","      <td>56.0</td>\n","      <td>69.0</td>\n","      <td>75.0</td>\n","      <td>67.0</td>\n","      <td>66.0</td>\n","      <td>76.0</td>\n","      <td>93.0</td>\n","      <td>88.0</td>\n","      <td>65.0</td>\n","      <td>56.0</td>\n","      <td>58.0</td>\n","      <td>63.0</td>\n","      <td>48.0</td>\n","      <td>29.0</td>\n","      <td>3.0</td>\n","      <td>-17.0</td>\n","      <td>-14.0</td>\n","      <td>-21.0</td>\n","      <td>-29.0</td>\n","      <td>-39.0</td>\n","      <td>-33.0</td>\n","      <td>-28.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>497</th>\n","      <td>-20.0</td>\n","      <td>-12.0</td>\n","      <td>-6.0</td>\n","      <td>6.0</td>\n","      <td>8.0</td>\n","      <td>-2.0</td>\n","      <td>-22.0</td>\n","      <td>-39.0</td>\n","      <td>-39.0</td>\n","      <td>-49.0</td>\n","      <td>-46.0</td>\n","      <td>-43.0</td>\n","      <td>-43.0</td>\n","      <td>-46.0</td>\n","      <td>-46.0</td>\n","      <td>-35.0</td>\n","      <td>-23.0</td>\n","      <td>-13.0</td>\n","      <td>-14.0</td>\n","      <td>-17.0</td>\n","      <td>-23.0</td>\n","      <td>-25.0</td>\n","      <td>-18.0</td>\n","      <td>-18.0</td>\n","      <td>-14.0</td>\n","      <td>-16.0</td>\n","      <td>-20.0</td>\n","      <td>-36.0</td>\n","      <td>-46.0</td>\n","      <td>-54.0</td>\n","      <td>-64.0</td>\n","      <td>-64.0</td>\n","      <td>-63.0</td>\n","      <td>-55.0</td>\n","      <td>-53.0</td>\n","      <td>-44.0</td>\n","      <td>-43.0</td>\n","      <td>-47.0</td>\n","      <td>-44.0</td>\n","      <td>-29.0</td>\n","      <td>...</td>\n","      <td>31.0</td>\n","      <td>26.0</td>\n","      <td>20.0</td>\n","      <td>21.0</td>\n","      <td>27.0</td>\n","      <td>33.0</td>\n","      <td>34.0</td>\n","      <td>42.0</td>\n","      <td>49.0</td>\n","      <td>37.0</td>\n","      <td>18.0</td>\n","      <td>-1.0</td>\n","      <td>-18.0</td>\n","      <td>-25.0</td>\n","      <td>-16.0</td>\n","      <td>6.0</td>\n","      <td>23.0</td>\n","      <td>37.0</td>\n","      <td>44.0</td>\n","      <td>39.0</td>\n","      <td>26.0</td>\n","      <td>14.0</td>\n","      <td>13.0</td>\n","      <td>11.0</td>\n","      <td>25.0</td>\n","      <td>38.0</td>\n","      <td>42.0</td>\n","      <td>31.0</td>\n","      <td>16.0</td>\n","      <td>8.0</td>\n","      <td>1.0</td>\n","      <td>7.0</td>\n","      <td>20.0</td>\n","      <td>19.0</td>\n","      <td>11.0</td>\n","      <td>10.0</td>\n","      <td>12.0</td>\n","      <td>9.0</td>\n","      <td>2.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>498</th>\n","      <td>-104.0</td>\n","      <td>-100.0</td>\n","      <td>-94.0</td>\n","      <td>-87.0</td>\n","      <td>-87.0</td>\n","      <td>-87.0</td>\n","      <td>-88.0</td>\n","      <td>-90.0</td>\n","      <td>-83.0</td>\n","      <td>-75.0</td>\n","      <td>-69.0</td>\n","      <td>-59.0</td>\n","      <td>-50.0</td>\n","      <td>-38.0</td>\n","      <td>-42.0</td>\n","      <td>-50.0</td>\n","      <td>-67.0</td>\n","      <td>-84.0</td>\n","      <td>-92.0</td>\n","      <td>-96.0</td>\n","      <td>-97.0</td>\n","      <td>-99.0</td>\n","      <td>-100.0</td>\n","      <td>-96.0</td>\n","      <td>-93.0</td>\n","      <td>-93.0</td>\n","      <td>-93.0</td>\n","      <td>-107.0</td>\n","      <td>-126.0</td>\n","      <td>-131.0</td>\n","      <td>-136.0</td>\n","      <td>-128.0</td>\n","      <td>-121.0</td>\n","      <td>-107.0</td>\n","      <td>-95.0</td>\n","      <td>-87.0</td>\n","      <td>-82.0</td>\n","      <td>-82.0</td>\n","      <td>-86.0</td>\n","      <td>-90.0</td>\n","      <td>...</td>\n","      <td>-28.0</td>\n","      <td>-22.0</td>\n","      <td>-23.0</td>\n","      <td>-19.0</td>\n","      <td>-20.0</td>\n","      <td>-18.0</td>\n","      <td>-14.0</td>\n","      <td>-23.0</td>\n","      <td>-37.0</td>\n","      <td>-56.0</td>\n","      <td>-64.0</td>\n","      <td>-67.0</td>\n","      <td>-64.0</td>\n","      <td>-49.0</td>\n","      <td>-40.0</td>\n","      <td>-34.0</td>\n","      <td>-39.0</td>\n","      <td>-39.0</td>\n","      <td>-43.0</td>\n","      <td>-43.0</td>\n","      <td>-35.0</td>\n","      <td>-33.0</td>\n","      <td>-31.0</td>\n","      <td>-24.0</td>\n","      <td>-11.0</td>\n","      <td>1.0</td>\n","      <td>8.0</td>\n","      <td>6.0</td>\n","      <td>2.0</td>\n","      <td>-8.0</td>\n","      <td>-16.0</td>\n","      <td>-27.0</td>\n","      <td>-38.0</td>\n","      <td>-45.0</td>\n","      <td>-40.0</td>\n","      <td>-30.0</td>\n","      <td>-37.0</td>\n","      <td>-45.0</td>\n","      <td>-59.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>499</th>\n","      <td>-43.0</td>\n","      <td>-57.0</td>\n","      <td>-86.0</td>\n","      <td>-106.0</td>\n","      <td>-106.0</td>\n","      <td>-120.0</td>\n","      <td>-111.0</td>\n","      <td>-88.0</td>\n","      <td>-81.0</td>\n","      <td>-90.0</td>\n","      <td>-109.0</td>\n","      <td>-102.0</td>\n","      <td>-69.0</td>\n","      <td>-20.0</td>\n","      <td>1.0</td>\n","      <td>25.0</td>\n","      <td>42.0</td>\n","      <td>68.0</td>\n","      <td>61.0</td>\n","      <td>36.0</td>\n","      <td>4.0</td>\n","      <td>4.0</td>\n","      <td>9.0</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>-2.0</td>\n","      <td>-13.0</td>\n","      <td>-23.0</td>\n","      <td>-30.0</td>\n","      <td>-39.0</td>\n","      <td>-13.0</td>\n","      <td>29.0</td>\n","      <td>48.0</td>\n","      <td>63.0</td>\n","      <td>67.0</td>\n","      <td>90.0</td>\n","      <td>96.0</td>\n","      <td>84.0</td>\n","      <td>81.0</td>\n","      <td>76.0</td>\n","      <td>...</td>\n","      <td>-129.0</td>\n","      <td>-117.0</td>\n","      <td>-130.0</td>\n","      <td>-132.0</td>\n","      <td>-134.0</td>\n","      <td>-150.0</td>\n","      <td>-161.0</td>\n","      <td>-191.0</td>\n","      <td>-180.0</td>\n","      <td>-145.0</td>\n","      <td>-135.0</td>\n","      <td>-123.0</td>\n","      <td>-139.0</td>\n","      <td>-127.0</td>\n","      <td>-103.0</td>\n","      <td>-111.0</td>\n","      <td>-95.0</td>\n","      <td>-82.0</td>\n","      <td>-55.0</td>\n","      <td>-19.0</td>\n","      <td>-11.0</td>\n","      <td>4.0</td>\n","      <td>16.0</td>\n","      <td>-22.0</td>\n","      <td>-1.0</td>\n","      <td>-20.0</td>\n","      <td>-11.0</td>\n","      <td>6.0</td>\n","      <td>17.0</td>\n","      <td>22.0</td>\n","      <td>3.0</td>\n","      <td>-9.0</td>\n","      <td>6.0</td>\n","      <td>39.0</td>\n","      <td>59.0</td>\n","      <td>80.0</td>\n","      <td>39.0</td>\n","      <td>21.0</td>\n","      <td>-8.0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>300 rows × 4097 columns</p>\n","</div>"],"text/plain":["         0      1      2      3      4  ...   4092   4093   4094   4095  tag\n","0    -18.0  -55.0 -126.0 -202.0 -238.0  ... -222.0 -224.0 -200.0 -127.0    2\n","1    -26.0    1.0   29.0   41.0   33.0  ... -205.0 -209.0 -207.0 -210.0    2\n","2     68.0 -106.0 -149.0 -141.0 -109.0  ...  554.0  460.0  343.0  247.0    2\n","3    343.0  311.0  284.0  274.0  260.0  ...  515.0  527.0  480.0  397.0    2\n","4    -63.0 -107.0 -208.0 -310.0 -395.0  ...  -32.0   56.0   44.0  -37.0    2\n","..     ...    ...    ...    ...    ...  ...    ...    ...    ...    ...  ...\n","495  213.0  210.0  210.0  212.0  194.0  ...   -8.0   -9.0   -2.0   20.0    2\n","496   24.0   10.0   -9.0  -18.0   -9.0  ...  -29.0  -39.0  -33.0  -28.0    0\n","497  -20.0  -12.0   -6.0    6.0    8.0  ...   10.0   12.0    9.0    2.0    0\n","498 -104.0 -100.0  -94.0  -87.0  -87.0  ...  -30.0  -37.0  -45.0  -59.0    1\n","499  -43.0  -57.0  -86.0 -106.0 -106.0  ...   80.0   39.0   21.0   -8.0    1\n","\n","[300 rows x 4097 columns]"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WlsWNsB87b1o","executionInfo":{"status":"ok","timestamp":1619609622274,"user_tz":-330,"elapsed":1669,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"60f8dedb-d9f5-4f22-8264-55643987444a"},"source":["df[\"tag\"].value_counts()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2    100\n","1    100\n","0    100\n","Name: tag, dtype: int64"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7yTzZWhL7bzq","executionInfo":{"status":"ok","timestamp":1619609622275,"user_tz":-330,"elapsed":1660,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"89efc477-8a72-4810-d52a-99fdef3a7646"},"source":["# Time Steps of LSTM\n","data_length = 4096\n","timesteps = 2048\n","data_dim = data_length//timesteps\n","data_dim\n","\n","\n","#breaking dataset into X nd y\n","df1=df.values     #df1 is numpy.ndarray and df is pandas.dataframe\n","X, y = df1[:, :-1], df1[:, -1]\n","print(df.shape)\n","print(\"shape of X\",X.shape)\n","print(\"shape of y\",y.shape)\n","\n","\n","#breaking X nd y into train nd test\n","from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1, stratify=y)\n","\n","print(\"\\nshape of X_train\",X_train.shape)\n","print(\"shape of X_test\",X_test.shape)\n","print(\"shape of y_train\",y_train.shape)\n","print(\"shape of y_test\",y_test.shape)\n","\n","X_train=X_train.reshape([X_train.shape[0], timesteps, data_dim])\n","X_test = X_test.reshape([X_test.shape[0], timesteps, data_dim])\n","y_train=np_utils.to_categorical(y_train, num_classes=3)\n","y_test=np_utils.to_categorical(y_test, num_classes=3)\n","\n","\n","\n","print(\"\\nshape of X_train\",X_train.shape)\n","print(\"shape of X_test\",X_test.shape)\n","print(\"shape of y_train\",y_train.shape)\n","print(\"shape of y_test\",y_test.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(300, 4097)\n","shape of X (300, 4096)\n","shape of y (300,)\n","\n","shape of X_train (210, 4096)\n","shape of X_test (90, 4096)\n","shape of y_train (210,)\n","shape of y_test (90,)\n","\n","shape of X_train (210, 2048, 2)\n","shape of X_test (90, 2048, 2)\n","shape of y_train (210, 3)\n","shape of y_test (90, 3)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"arKBgGVY7bxI","executionInfo":{"status":"ok","timestamp":1619609695333,"user_tz":-330,"elapsed":74709,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"d2eadb14-508c-48ec-d635-ab6dadc06e3a"},"source":["nb_epoch=100\n","\n","model = Sequential()\n","\n","model.add(Bidirectional(LSTM(80, input_shape= (timesteps, data_dim), return_sequences = True)))\n","model.add(Dropout(0.1))\n","\n","model.add(TimeDistributed(Dense(50)))\n","\n","model.add(GlobalAveragePooling1D())\n","\n","model.add(Dense(3, activation='softmax'))\n","model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[sensitivity, specificity, 'accuracy'])\n","#print(model.summary())\n","history = model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=64, epochs=nb_epoch)\n","\n","\n","plt.plot(history.history['specificity'], 'b--')\n","plt.plot(history.history['sensitivity'], 'g--')\n","plt.plot(history.history['accuracy'], 'r--')\n","plt.title('Model Different Metrics')\n","plt.ylabel('Metrics')\n","plt.xlabel('Epoch #')\n","plt.show()\n","\n","\n","\n","scores = model.evaluate(X_test, y_test, verbose=0)\n","print(\"Sensitivity = %.2f%%\" % (scores[1]*100))\n","print(\"Specificity = %.2f%%\" % (scores[2]*100))\n","print(\"Classification Accuracy = %.2f%%\" % (scores[3]*100))\n","\n","\n","%matplotlib inline\n","plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","plt.title('model accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.grid()\n","plt.show()\n","\n","plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.grid()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch 1/100\n","4/4 [==============================] - 5s 423ms/step - loss: 1.0586 - sensitivity: 0.0108 - specificity: 0.9946 - accuracy: 0.4297 - val_loss: 0.8748 - val_sensitivity: 0.1004 - val_specificity: 1.0000 - val_accuracy: 0.8778\n","Epoch 2/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.8447 - sensitivity: 0.2481 - specificity: 0.9878 - accuracy: 0.8160 - val_loss: 0.7362 - val_sensitivity: 0.4922 - val_specificity: 0.9865 - val_accuracy: 0.6778\n","Epoch 3/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.7265 - sensitivity: 0.4944 - specificity: 0.9678 - accuracy: 0.6833 - val_loss: 0.6295 - val_sensitivity: 0.5042 - val_specificity: 0.9826 - val_accuracy: 0.8333\n","Epoch 4/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.6258 - sensitivity: 0.5089 - specificity: 0.9772 - accuracy: 0.8314 - val_loss: 0.5511 - val_sensitivity: 0.6238 - val_specificity: 0.9691 - val_accuracy: 0.9111\n","Epoch 5/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.5604 - sensitivity: 0.6512 - specificity: 0.9740 - accuracy: 0.8540 - val_loss: 0.4880 - val_sensitivity: 0.8407 - val_specificity: 0.9691 - val_accuracy: 0.9111\n","Epoch 6/100\n","4/4 [==============================] - 1s 173ms/step - loss: 0.4897 - sensitivity: 0.7384 - specificity: 0.9636 - accuracy: 0.8788 - val_loss: 0.4407 - val_sensitivity: 0.8642 - val_specificity: 0.9691 - val_accuracy: 0.9000\n","Epoch 7/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.4632 - sensitivity: 0.8089 - specificity: 0.9479 - accuracy: 0.8494 - val_loss: 0.4019 - val_sensitivity: 0.8876 - val_specificity: 0.9651 - val_accuracy: 0.9111\n","Epoch 8/100\n","4/4 [==============================] - 1s 173ms/step - loss: 0.4012 - sensitivity: 0.8771 - specificity: 0.9544 - accuracy: 0.9008 - val_loss: 0.3789 - val_sensitivity: 0.8876 - val_specificity: 0.9651 - val_accuracy: 0.9111\n","Epoch 9/100\n","4/4 [==============================] - 1s 173ms/step - loss: 0.3656 - sensitivity: 0.8974 - specificity: 0.9696 - accuracy: 0.9194 - val_loss: 0.3615 - val_sensitivity: 0.9069 - val_specificity: 0.9748 - val_accuracy: 0.9111\n","Epoch 10/100\n","4/4 [==============================] - 1s 171ms/step - loss: 0.3428 - sensitivity: 0.9080 - specificity: 0.9787 - accuracy: 0.9275 - val_loss: 0.3208 - val_sensitivity: 0.9069 - val_specificity: 0.9612 - val_accuracy: 0.9111\n","Epoch 11/100\n","4/4 [==============================] - 1s 172ms/step - loss: 0.2968 - sensitivity: 0.9210 - specificity: 0.9701 - accuracy: 0.9412 - val_loss: 0.2860 - val_sensitivity: 0.9147 - val_specificity: 0.9612 - val_accuracy: 0.9111\n","Epoch 12/100\n","4/4 [==============================] - 1s 175ms/step - loss: 0.2866 - sensitivity: 0.9080 - specificity: 0.9681 - accuracy: 0.9180 - val_loss: 0.2644 - val_sensitivity: 0.9339 - val_specificity: 0.9709 - val_accuracy: 0.9222\n","Epoch 13/100\n","4/4 [==============================] - 1s 176ms/step - loss: 0.2667 - sensitivity: 0.9344 - specificity: 0.9747 - accuracy: 0.9367 - val_loss: 0.2556 - val_sensitivity: 0.9417 - val_specificity: 0.9787 - val_accuracy: 0.9444\n","Epoch 14/100\n","4/4 [==============================] - 1s 174ms/step - loss: 0.2276 - sensitivity: 0.9293 - specificity: 0.9794 - accuracy: 0.9551 - val_loss: 0.2298 - val_sensitivity: 0.9417 - val_specificity: 0.9787 - val_accuracy: 0.9444\n","Epoch 15/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.2035 - sensitivity: 0.9424 - specificity: 0.9808 - accuracy: 0.9526 - val_loss: 0.1942 - val_sensitivity: 0.9495 - val_specificity: 0.9826 - val_accuracy: 0.9444\n","Epoch 16/100\n","4/4 [==============================] - 1s 190ms/step - loss: 0.1778 - sensitivity: 0.9408 - specificity: 0.9740 - accuracy: 0.9483 - val_loss: 0.1779 - val_sensitivity: 0.9417 - val_specificity: 0.9748 - val_accuracy: 0.9333\n","Epoch 17/100\n","4/4 [==============================] - 1s 185ms/step - loss: 0.1610 - sensitivity: 0.9467 - specificity: 0.9747 - accuracy: 0.9509 - val_loss: 0.1404 - val_sensitivity: 0.9651 - val_specificity: 0.9865 - val_accuracy: 0.9667\n","Epoch 18/100\n","4/4 [==============================] - 1s 174ms/step - loss: 0.1365 - sensitivity: 0.9524 - specificity: 0.9762 - accuracy: 0.9570 - val_loss: 0.1535 - val_sensitivity: 0.9573 - val_specificity: 0.9787 - val_accuracy: 0.9556\n","Epoch 19/100\n","4/4 [==============================] - 1s 181ms/step - loss: 0.1339 - sensitivity: 0.9505 - specificity: 0.9786 - accuracy: 0.9516 - val_loss: 0.1432 - val_sensitivity: 0.9495 - val_specificity: 0.9787 - val_accuracy: 0.9556\n","Epoch 20/100\n","4/4 [==============================] - 1s 176ms/step - loss: 0.1379 - sensitivity: 0.9481 - specificity: 0.9805 - accuracy: 0.9560 - val_loss: 0.1234 - val_sensitivity: 0.9573 - val_specificity: 0.9865 - val_accuracy: 0.9667\n","Epoch 21/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0982 - sensitivity: 0.9599 - specificity: 0.9820 - accuracy: 0.9733 - val_loss: 0.1115 - val_sensitivity: 0.9573 - val_specificity: 0.9826 - val_accuracy: 0.9667\n","Epoch 22/100\n","4/4 [==============================] - 1s 181ms/step - loss: 0.1167 - sensitivity: 0.9536 - specificity: 0.9768 - accuracy: 0.9502 - val_loss: 0.0900 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 23/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.1047 - sensitivity: 0.9469 - specificity: 0.9807 - accuracy: 0.9438 - val_loss: 0.1270 - val_sensitivity: 0.9537 - val_specificity: 0.9808 - val_accuracy: 0.9667\n","Epoch 24/100\n","4/4 [==============================] - 1s 174ms/step - loss: 0.0797 - sensitivity: 0.9792 - specificity: 0.9917 - accuracy: 0.9820 - val_loss: 0.2559 - val_sensitivity: 0.9225 - val_specificity: 0.9612 - val_accuracy: 0.9222\n","Epoch 25/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.1242 - sensitivity: 0.9611 - specificity: 0.9806 - accuracy: 0.9631 - val_loss: 0.1084 - val_sensitivity: 0.9573 - val_specificity: 0.9826 - val_accuracy: 0.9556\n","Epoch 26/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.1032 - sensitivity: 0.9545 - specificity: 0.9813 - accuracy: 0.9598 - val_loss: 0.1472 - val_sensitivity: 0.9303 - val_specificity: 0.9651 - val_accuracy: 0.9333\n","Epoch 27/100\n","4/4 [==============================] - 1s 176ms/step - loss: 0.0895 - sensitivity: 0.9719 - specificity: 0.9896 - accuracy: 0.9775 - val_loss: 0.1330 - val_sensitivity: 0.9381 - val_specificity: 0.9730 - val_accuracy: 0.9556\n","Epoch 28/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.0753 - sensitivity: 0.9705 - specificity: 0.9893 - accuracy: 0.9773 - val_loss: 0.1840 - val_sensitivity: 0.9459 - val_specificity: 0.9730 - val_accuracy: 0.9556\n","Epoch 29/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.0779 - sensitivity: 0.9755 - specificity: 0.9891 - accuracy: 0.9761 - val_loss: 0.1475 - val_sensitivity: 0.9495 - val_specificity: 0.9748 - val_accuracy: 0.9444\n","Epoch 30/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0693 - sensitivity: 0.9830 - specificity: 0.9915 - accuracy: 0.9860 - val_loss: 0.0973 - val_sensitivity: 0.9651 - val_specificity: 0.9865 - val_accuracy: 0.9667\n","Epoch 31/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.0724 - sensitivity: 0.9812 - specificity: 0.9906 - accuracy: 0.9802 - val_loss: 0.0678 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 32/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.0642 - sensitivity: 0.9833 - specificity: 0.9917 - accuracy: 0.9820 - val_loss: 0.0924 - val_sensitivity: 0.9537 - val_specificity: 0.9769 - val_accuracy: 0.9667\n","Epoch 33/100\n","4/4 [==============================] - 1s 175ms/step - loss: 0.0544 - sensitivity: 0.9875 - specificity: 0.9938 - accuracy: 0.9865 - val_loss: 0.1462 - val_sensitivity: 0.9303 - val_specificity: 0.9691 - val_accuracy: 0.9333\n","Epoch 34/100\n","4/4 [==============================] - 1s 180ms/step - loss: 0.0505 - sensitivity: 0.9851 - specificity: 0.9925 - accuracy: 0.9877 - val_loss: 0.0781 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 35/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.0508 - sensitivity: 0.9818 - specificity: 0.9909 - accuracy: 0.9804 - val_loss: 0.1211 - val_sensitivity: 0.9459 - val_specificity: 0.9730 - val_accuracy: 0.9556\n","Epoch 36/100\n","4/4 [==============================] - 1s 175ms/step - loss: 0.0486 - sensitivity: 0.9872 - specificity: 0.9936 - accuracy: 0.9905 - val_loss: 0.0759 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 37/100\n","4/4 [==============================] - 1s 175ms/step - loss: 0.0396 - sensitivity: 0.9932 - specificity: 0.9966 - accuracy: 0.9925 - val_loss: 0.0612 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 38/100\n","4/4 [==============================] - 1s 182ms/step - loss: 0.0407 - sensitivity: 0.9859 - specificity: 0.9930 - accuracy: 0.9849 - val_loss: 0.1073 - val_sensitivity: 0.9537 - val_specificity: 0.9769 - val_accuracy: 0.9667\n","Epoch 39/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.0385 - sensitivity: 0.9974 - specificity: 0.9987 - accuracy: 0.9971 - val_loss: 0.1460 - val_sensitivity: 0.9423 - val_specificity: 0.9712 - val_accuracy: 0.9667\n","Epoch 40/100\n","4/4 [==============================] - 1s 176ms/step - loss: 0.0328 - sensitivity: 0.9932 - specificity: 0.9966 - accuracy: 0.9925 - val_loss: 0.1256 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 41/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.0326 - sensitivity: 0.9885 - specificity: 0.9943 - accuracy: 0.9879 - val_loss: 0.1371 - val_sensitivity: 0.9423 - val_specificity: 0.9712 - val_accuracy: 0.9667\n","Epoch 42/100\n","4/4 [==============================] - 1s 183ms/step - loss: 0.0237 - sensitivity: 0.9974 - specificity: 0.9987 - accuracy: 0.9971 - val_loss: 0.0634 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 43/100\n","4/4 [==============================] - 1s 182ms/step - loss: 0.0297 - sensitivity: 0.9901 - specificity: 0.9951 - accuracy: 0.9894 - val_loss: 0.0508 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 44/100\n","4/4 [==============================] - 1s 181ms/step - loss: 0.0590 - sensitivity: 0.9812 - specificity: 0.9906 - accuracy: 0.9802 - val_loss: 0.0715 - val_sensitivity: 0.9730 - val_specificity: 0.9865 - val_accuracy: 0.9778\n","Epoch 45/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0179 - sensitivity: 0.9974 - specificity: 0.9987 - accuracy: 0.9971 - val_loss: 0.1489 - val_sensitivity: 0.9573 - val_specificity: 0.9787 - val_accuracy: 0.9556\n","Epoch 46/100\n","4/4 [==============================] - 1s 180ms/step - loss: 0.0553 - sensitivity: 0.9658 - specificity: 0.9829 - accuracy: 0.9677 - val_loss: 0.0603 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 47/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.0454 - sensitivity: 0.9901 - specificity: 0.9951 - accuracy: 0.9894 - val_loss: 0.0708 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 48/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0330 - sensitivity: 0.9927 - specificity: 0.9964 - accuracy: 0.9924 - val_loss: 0.0864 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 49/100\n","4/4 [==============================] - 1s 183ms/step - loss: 0.0340 - sensitivity: 0.9974 - specificity: 0.9987 - accuracy: 0.9971 - val_loss: 0.0797 - val_sensitivity: 0.9730 - val_specificity: 0.9865 - val_accuracy: 0.9778\n","Epoch 50/100\n","4/4 [==============================] - 1s 182ms/step - loss: 0.0355 - sensitivity: 0.9845 - specificity: 0.9923 - accuracy: 0.9875 - val_loss: 0.0501 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 51/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.0229 - sensitivity: 0.9958 - specificity: 0.9979 - accuracy: 0.9955 - val_loss: 0.1186 - val_sensitivity: 0.9730 - val_specificity: 0.9865 - val_accuracy: 0.9778\n","Epoch 52/100\n","4/4 [==============================] - 1s 181ms/step - loss: 0.0283 - sensitivity: 0.9927 - specificity: 0.9964 - accuracy: 0.9924 - val_loss: 0.1111 - val_sensitivity: 0.9730 - val_specificity: 0.9865 - val_accuracy: 0.9778\n","Epoch 53/100\n","4/4 [==============================] - 1s 182ms/step - loss: 0.0261 - sensitivity: 0.9927 - specificity: 0.9964 - accuracy: 0.9924 - val_loss: 0.0880 - val_sensitivity: 0.9730 - val_specificity: 0.9865 - val_accuracy: 0.9778\n","Epoch 54/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.0176 - sensitivity: 0.9901 - specificity: 0.9951 - accuracy: 0.9894 - val_loss: 0.0914 - val_sensitivity: 0.9730 - val_specificity: 0.9865 - val_accuracy: 0.9778\n","Epoch 55/100\n","4/4 [==============================] - 1s 176ms/step - loss: 0.0113 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0944 - val_sensitivity: 0.9730 - val_specificity: 0.9865 - val_accuracy: 0.9778\n","Epoch 56/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.0152 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0919 - val_sensitivity: 0.9730 - val_specificity: 0.9865 - val_accuracy: 0.9778\n","Epoch 57/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.0099 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0524 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 58/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.0116 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0605 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 59/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.0082 - sensitivity: 0.9944 - specificity: 0.9972 - accuracy: 0.9981 - val_loss: 0.0717 - val_sensitivity: 0.9730 - val_specificity: 0.9865 - val_accuracy: 0.9778\n","Epoch 60/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0107 - sensitivity: 0.9944 - specificity: 0.9972 - accuracy: 0.9981 - val_loss: 0.0459 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 61/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.0112 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0871 - val_sensitivity: 0.9730 - val_specificity: 0.9865 - val_accuracy: 0.9778\n","Epoch 62/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.0236 - sensitivity: 0.9872 - specificity: 0.9936 - accuracy: 0.9905 - val_loss: 0.0729 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 63/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.0172 - sensitivity: 0.9974 - specificity: 0.9987 - accuracy: 0.9971 - val_loss: 0.1183 - val_sensitivity: 0.9651 - val_specificity: 0.9826 - val_accuracy: 0.9667\n","Epoch 64/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0269 - sensitivity: 0.9927 - specificity: 0.9964 - accuracy: 0.9924 - val_loss: 0.0472 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 65/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0177 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0836 - val_sensitivity: 0.9537 - val_specificity: 0.9769 - val_accuracy: 0.9667\n","Epoch 66/100\n","4/4 [==============================] - 1s 180ms/step - loss: 0.0156 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0841 - val_sensitivity: 0.9537 - val_specificity: 0.9769 - val_accuracy: 0.9667\n","Epoch 67/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0234 - sensitivity: 0.9859 - specificity: 0.9930 - accuracy: 0.9849 - val_loss: 0.0714 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 68/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.0178 - sensitivity: 0.9918 - specificity: 0.9959 - accuracy: 0.9951 - val_loss: 0.0697 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 69/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.0132 - sensitivity: 0.9974 - specificity: 0.9987 - accuracy: 0.9971 - val_loss: 0.0582 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 70/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.0222 - sensitivity: 0.9927 - specificity: 0.9964 - accuracy: 0.9924 - val_loss: 0.0530 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 71/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.0283 - sensitivity: 0.9958 - specificity: 0.9979 - accuracy: 0.9955 - val_loss: 0.0558 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 72/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0169 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0568 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 73/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.0146 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0727 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 74/100\n","4/4 [==============================] - 1s 181ms/step - loss: 0.0138 - sensitivity: 0.9927 - specificity: 0.9964 - accuracy: 0.9924 - val_loss: 0.0675 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 75/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0091 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0630 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 76/100\n","4/4 [==============================] - 1s 188ms/step - loss: 0.0107 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0555 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 77/100\n","4/4 [==============================] - 1s 182ms/step - loss: 0.0090 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0593 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 78/100\n","4/4 [==============================] - 1s 186ms/step - loss: 0.0082 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0676 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 79/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0069 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0703 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 80/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0090 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0660 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 81/100\n","4/4 [==============================] - 1s 181ms/step - loss: 0.0085 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0629 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 82/100\n","4/4 [==============================] - 1s 182ms/step - loss: 0.0060 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0537 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 83/100\n","4/4 [==============================] - 1s 181ms/step - loss: 0.0065 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0606 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 84/100\n","4/4 [==============================] - 1s 180ms/step - loss: 0.0045 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0632 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 85/100\n","4/4 [==============================] - 1s 181ms/step - loss: 0.0046 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0634 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 86/100\n","4/4 [==============================] - 1s 181ms/step - loss: 0.0040 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0531 - val_sensitivity: 0.9808 - val_specificity: 0.9904 - val_accuracy: 0.9889\n","Epoch 87/100\n","4/4 [==============================] - 1s 182ms/step - loss: 0.0044 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0495 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 88/100\n","4/4 [==============================] - 1s 185ms/step - loss: 0.0049 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0552 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 89/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.0052 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0748 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 90/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0052 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0744 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 91/100\n","4/4 [==============================] - 1s 184ms/step - loss: 0.0029 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0727 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 92/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0041 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0702 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 93/100\n","4/4 [==============================] - 1s 178ms/step - loss: 0.0033 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0707 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 94/100\n","4/4 [==============================] - 1s 175ms/step - loss: 0.0034 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0728 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 95/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.0029 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0714 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 96/100\n","4/4 [==============================] - 1s 177ms/step - loss: 0.0030 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0716 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 97/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0034 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0694 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 98/100\n","4/4 [==============================] - 1s 179ms/step - loss: 0.0021 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0689 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 99/100\n","4/4 [==============================] - 1s 181ms/step - loss: 0.0023 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0683 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n","Epoch 100/100\n","4/4 [==============================] - 1s 176ms/step - loss: 0.0020 - sensitivity: 1.0000 - specificity: 1.0000 - accuracy: 1.0000 - val_loss: 0.0680 - val_sensitivity: 0.9615 - val_specificity: 0.9808 - val_accuracy: 0.9778\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Sensitivity = 97.44%\n","Specificity = 98.72%\n","Classification Accuracy = 97.78%\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KJGtDOMZ7brn","executionInfo":{"status":"ok","timestamp":1619609695947,"user_tz":-330,"elapsed":75315,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"1406ac7b-3163-4020-dfb7-6f1674be21b2"},"source":["#check output of model\n","y_predict=model.predict(X_test)\n","\n","# define vector\n","probs = asarray(y_predict)\n","print(probs.shape)\n","# get argmax\n","result = argmax(probs, axis=1)\n","print(result)\n","\n","y_test_cm=argmax(y_test,axis=1)\n","print(result.shape)\n","\n","\n","\n","confusion = confusion_matrix(y_test_cm, result)\n","print('Confusion Matrix\\n')\n","print(confusion)\n","\n","#importing accuracy_score, precision_score, recall_score, f1_score\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","print('\\nAccuracy: {:.2f}\\n'.format(accuracy_score(y_test_cm, result)))\n","\n","print('Micro Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='micro')))\n","print('Micro Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='micro')))\n","print('Micro F1-score: {:.2f}\\n'.format(f1_score(y_test_cm, result, average='micro')))\n","\n","print('Macro Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='macro')))\n","print('Macro Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='macro')))\n","print('Macro F1-score: {:.2f}\\n'.format(f1_score(y_test_cm, result, average='macro')))\n","\n","print('Weighted Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='weighted')))\n","print('Weighted Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='weighted')))\n","print('Weighted F1-score: {:.2f}'.format(f1_score(y_test_cm, result, average='weighted')))\n","\n","from sklearn.metrics import classification_report\n","print('\\nClassification Report\\n')\n","print(classification_report(y_test_cm, result, target_names=['Class A', 'Class C', 'Class E']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(90, 3)\n","[2 2 0 0 0 1 1 1 2 0 0 0 2 1 0 2 0 0 1 0 2 1 0 2 2 1 0 0 2 1 2 1 1 2 0 2 1\n"," 1 1 0 1 1 2 0 0 1 2 0 2 1 2 2 1 1 2 0 2 1 0 1 2 0 1 0 0 2 2 2 0 2 1 2 0 2\n"," 0 1 1 1 2 1 0 1 2 0 2 0 0 2 2 0]\n","(90,)\n","Confusion Matrix\n","\n","[[30  0  0]\n"," [ 1 28  1]\n"," [ 0  0 30]]\n","\n","Accuracy: 0.98\n","\n","Micro Precision: 0.98\n","Micro Recall: 0.98\n","Micro F1-score: 0.98\n","\n","Macro Precision: 0.98\n","Macro Recall: 0.98\n","Macro F1-score: 0.98\n","\n","Weighted Precision: 0.98\n","Weighted Recall: 0.98\n","Weighted F1-score: 0.98\n","\n","Classification Report\n","\n","              precision    recall  f1-score   support\n","\n","     Class A       0.97      1.00      0.98        30\n","     Class C       1.00      0.93      0.97        30\n","     Class E       0.97      1.00      0.98        30\n","\n","    accuracy                           0.98        90\n","   macro avg       0.98      0.98      0.98        90\n","weighted avg       0.98      0.98      0.98        90\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"B8vPIqbrECph","executionInfo":{"status":"ok","timestamp":1619609695948,"user_tz":-330,"elapsed":75308,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"913a20d6-fef9-41b5-f1df-e7a6b711e64e"},"source":["# serialize model to JSON\n","model_json = model.to_json()\n","with open(\"/content/drive/MyDrive/mtech_finalyr_project/3 class problem/model_3class_AvsCvsE.json\", \"w\") as json_file:\n","    json_file.write(model_json)\n","# serialize weights to HDF5\n","model.save_weights(\"/content/drive/MyDrive/mtech_finalyr_project/3 class problem/model_3class_AvsCvsE.h5\")\n","print(\"Saved model to disk\")\n"," "],"execution_count":null,"outputs":[{"output_type":"stream","text":["Saved model to disk\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"aBYWhb7OTt9c"},"source":["\"\"\"\n","# load json and create model\n","json_file = open('model.json', 'r')\n","loaded_model_json = json_file.read()\n","json_file.close()\n","loaded_model = model_from_json(loaded_model_json)\n","# load weights into new model\n","loaded_model.load_weights(\"model.h5\")\n","print(\"Loaded model from disk\")\n"," \n","# evaluate loaded model on test data\n","loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n","score = loaded_model.evaluate(X, Y, verbose=0)\n","\"\"\""],"execution_count":null,"outputs":[]}]}